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Tytuł:
Ensembles of instance selection methods: A comparative study
Autorzy:
Blachnik, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/330413.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
machine learning
instance selection
ensemble methods
uczenie maszynowe
selekcja przypadków
metoda zespołowa
Opis:
Instance selection is often performed as one of the preprocessing methods which, along with feature selection, allows a significant reduction in computational complexity and an increase in prediction accuracy. So far, only few authors have considered ensembles of instance selection methods, while the ensembles of final predictive models attract many researchers. To bridge that gap, in this paper we compare four ensembles adapted to instance selection: Bagging, Feature Bagging, AdaBoost and Additive Noise. The last one is introduced for the first time in this paper. The study is based on empirical comparison performed on 43 datasets and 9 base instance selection methods. The experiments are divided into three scenarios. In the first one, evaluated on a single dataset, we demonstrate the influence of the ensembles on the compression–accuracy relation, in the second scenario the goal is to achieve the highest prediction accuracy, and in the third one both accuracy and the level of dataset compression constitute a multi-objective criterion. The obtained results indicate that ensembles of instance selection improve the base instance selection algorithms except for unstable methods such as CNN and IB3, which is achieved at the expense of compression. In the comparison, Bagging and AdaBoost lead in most of the scenarios. In the experiments we evaluate three classifiers: 1NN, kNN and SVM. We also note a deterioration in prediction accuracy for robust classifiers (kNN and SVM) trained on data filtered by any instance selection methods (including the ensembles) when compared with the results obtained when the entire training set was used to train these classifiers.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 1; 151-168
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Characterization of symbolic rules embedded in deep DIMLP networks : a challenge to transparency of deep learning
Autorzy:
Bologna, G.
Hayashi, Y.
Powiązania:
https://bibliotekanauki.pl/articles/91545.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
ensemble
Deep Learning
rule extraction
feature detectors
Opis:
Rule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 4; 265-286
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection
Autorzy:
Brunner, Csaba
Kő, Andrea
Fodor, Szabina
Powiązania:
https://bibliotekanauki.pl/articles/2147134.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
intrusion detection
neural network
ensemble classifiers
hyperparameter optimization
sparse autoencoder
NSL-KDD
machine learning
Opis:
Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 149--163
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Real-time equipment condition assessment for a class-imbalanced dataset based on heterogeneous ensemble learning
Ocena stanu sprzętu w czasie rzeczywistym dla zbiorów danych o niezrównoważonym rozkładzie w klasach. Metoda oparta na uczeniu zespołowym
Autorzy:
Chen, Xiaohui
Zhang, Zhiyao
Zhang, Ze
Powiązania:
https://bibliotekanauki.pl/articles/300613.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
condition assessment
heterogeneous ensemble learning
genetic algorithm
class-imbalanced
ocena stanu
uczenie zespołowe
algorytm genetyczny
niezrównoważony rozkład w klasach
Opis:
This study proposes an ensemble learning model for the purpose of performing a real-time equipment condition assessment. This model makes it possible to plan desired preventive maintenance activities before an unexpected failure takes place. This study focuses on the class-imbalanced problem in equipment condition assessment research. In reality, equipment will experience multiple conditions(states), most of the time remaining in the normal condition and relatively rarely being in the critical condition, which means that, from the perspective of data modelling, the distribution of samples is highly imbalanced among different classes(conditions). The majority of samples belong to the normal condition, while the minority belong to the critical condition, which poses a great challenge to the classification performance. To address this problem, a genetic algorithm-based ensemble learning model is presented. Furthermore, a self-updating learning strategy is presented for online monitoring, contributing to adaptability and reliability enhancement along with time. Many previous studies have attempted feature extraction and to set thresholds for equipment health indicators. This study has an advantage of omitting these steps, as it can directly assess the equipment condition through the proposed ensemble learning model. Numerical experiments, including two types of comparison studies, have been conducted. The results show the greater effectiveness of our proposed model over that of previous research in terms of the stability and accuracy of its classification performance.
W pracy przedstawiono model uczenia maszynowego opartego na zespołach niejednorodnych klasyfikatorów (ensemble learning), który pozwala przeprowadzać ocenę stanu sprzętu w czasie rzeczywistym. Model ten umożliwia zaplanowanie niezbędnych czynności konserwacji profilaktycznej przed wystąpieniem niespodziewanego uszkodzenia. Tematem pracy jest zagadnienie niezrównoważonego rozkładu w klasach poruszane w badaniach dotyczących oceny stanu sprzętu. W warunkach rzeczywistych, sprzęt chrakteryzuje wiele różnych stanów, przy czym przez większość czasu pozostaje on w stanie normalnym, a relatywnie rzadko znajduje się w stanie krytycznym, co oznacza, że z punktu widzenia modelowania danych, rozkład prób w poszczególnych klasach (stanach) jest wysoce niezrównoważony. Większość prób należy do stanu normalnego, a mniejszość do stanu krytycznego, co stanowi duże wyzwanie jeśli chodzi o wydajność klasyfikacji. W celu rozwiązania tego problemu, przedstawiono model uczenia zespołowego oparty na algorytmie genetycznym. Ponadto zaprezentowano samoaktualizującą się strategię uczenia wykorzystywaną do monitorowania online, która wraz z upływem czasu zwiększa adaptacyjność i niezawodność modelu . W wielu poprzednich badaniach podejmowano próby ekstrakcji cech oraz ustalania progów dla wskaźników stanu sprzętu. Zaletą przedstawionej metody jest to, że pozwala ona pominąć te etapy i bezpośrednio oceniać stan sprzętu za pomocą proponowanego modelu uczenia zespołowego. Przeprowadzono eksperymenty numeryczne, w tym dwa rodzaje badań porównawczych. Wyniki pokazują większą skuteczność proponowanego modelu w stosunku do poprzednich badań pod względem stabilności i trafności klasyfikacji.
Źródło:
Eksploatacja i Niezawodność; 2019, 21, 1; 68-80
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Imitation learning of car driving skills with decision trees and random forests
Autorzy:
Cichosz, P.
Pawełczak, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/329901.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
imitation learning
behavioral cloning
model ensemble
random forest
control
autonomous driving
car racing
decision tree
drzewo decyzyjne
lasy losowe
sterowanie
wyścigi samochodowe
Opis:
Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble of classifiers based on deep learning for medical image recognition
Autorzy:
Gil, Fabian
Osowski, Stanisław
Świderski, Bartosz
Słowińska, Monika
Powiązania:
https://bibliotekanauki.pl/articles/2203370.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
breast cancer
CNN
deep learning
ensemble of classifiers
feature selection
melanoma
Opis:
The paper presents special forms of an ensemble of classifiers for analysis of medical images based on application of deep learning. The study analyzes different structures of convolutional neural networks applied in the recognition of two types of medical images: dermoscopic images for melanoma and mammograms for breast cancer. Two approaches to ensemble creation are proposed. In the first approach, the images are processed by a convolutional neural network and the flattened vector of image descriptors is subjected to feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent input attributes for the classical classifiers: support vector machine, a random forest of decision trees, and softmax. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. In the second approach, different structures of convolutional neural networks are directly applied as the members of the ensemble. The efficiency of the proposed classification systems is investigated and compared to medical data representing dermoscopic images of melanoma and breast cancer mammogram images. Thanks to fusion of the results of many classifiers forming an ensemble, accuracy and all other quality measures have been significantly increased for both types of medical images.
Źródło:
Metrology and Measurement Systems; 2023, 30, 1; 139--156
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Three Dimensional Empirical Study of Logging Questions From Six Popular Q & A Websites
Autorzy:
Gujral, Harshit
Sharma, Abhinav
Lal, Sangeeta
Kumar, Lov
Powiązania:
https://bibliotekanauki.pl/articles/384178.pdf
Data publikacji:
2019
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
classification
debugging
ensemble
logging
machine learning
source code analysis
tracing
Opis:
Background: Q&A websites such as StackOverflow or Serverfault provide an open platform for users to ask questions and to get help from experts present worldwide. These websites not only help users by answering their questions but also act as a knowledge base. These data present on these websites can be mined to extract valuable information that can benefit the software practitioners. Software engineering research community has already understood the potential benefits of mining data from Q&A websites and several research studies have already been conducted in this area. Aim: The aim of the study presented in this paper is to perform an empirical analysis of logging questions from six popular Q&A websites. Method: We perform statistical, programming language and content analysis of logging questions. Our analysis helped us to gain insight about the logging discussion happening in six different domains of the StackExchange websites. Results: Our analysis provides insight about the logging issues of software practitioners: logging questions are pervasive in all the Q&A websites, the mean time to get accepted answer for logging questions on SU and SF websites are much higher as compared to other websites, a large number of logging question invite a great amount of discussion in the SoftwareEngineering Q&A website, most of the logging issues occur in C++ and Java, the trend for number of logging questions is increasing for Java, Python, and Javascript, whereas, it is decreasing or constant for C, C++, C#, for the ServerFault and Superuser website 'C' is the dominant programming language.
Źródło:
e-Informatica Software Engineering Journal; 2019, 13, 1; 105-139
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative study on performance of basic and ensemble classifiers with various datasets
Autorzy:
Gunakala, Archana
Shahid, Afzal Hussain
Powiązania:
https://bibliotekanauki.pl/articles/30148255.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
classification
Naïve Bayes
neural network
Support Vector Machine
Decision Tree
ensemble learning
Random Forest
Opis:
Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.
Źródło:
Applied Computer Science; 2023, 19, 1; 107-132
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Projekt badawczy, uczenie zespołowe, teoria ugruntowana
Realization of research project in the light of team learning practice
Autorzy:
Janiszewski, A.
Powiązania:
https://bibliotekanauki.pl/articles/323831.pdf
Data publikacji:
2018
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
projekt badawczy
uczenie zespołowe
teoria ugruntowana
research project
ensemble learning
grounded theory
Opis:
W artykule podjęto tematykę uczenia się w zespole. Omówiono zagadnienia teoretyczne, propozycję budowy narzędzia badawczego oraz wyniki badania wstępnego. Usiłowano uzyskać odpowiedź na pytanie badawcze dotyczące sposobów i środków wykorzystywanych przez członków zespołu badawczego dążących do uzyskania namacalnych wyników pracy w warunkach intensywnych i wielokierunkowych przepływów wiedzy. Uwaga została skoncentrowana na specyfice pracy zespołów realizujących projekt badawczy dofinansowywany przez Narodowe Centrum Nauki. Uzyskane wyniki mogą posłużyć w przyszłości jako podstawa do przeprowadzenia badań związanych z funkcjonowaniem zespołów badawczych NCN w całym kraju, co z uwagi na obecne trendy w zakresie realizacji badań można ocenić jako przedsięwzięcie warte podjęcia.
In the paper author discusses the topic connected with team learning issues. Theoretical problems are analysed as well as the proposition of the way of building of a research tool is presented. Next, results of initial research are shown. The authors tries to find the answer for the research question related to the ways and means which are made use of by team members who deal with intensive and multidirectional knowledge flows in order to obtain some tangible results of their work. The research object which attention is paid to is research teams that carry out research projects financed by National Science Centre. Hitherto obtained results may allow author to conduct research on NCN’s research teams in all country in the future. Taking into account current trends as to ways of conducting research this kind of undertaking appears to be worth implementing.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2018, 117; 187-198
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-label classification using error correcting output codes
Autorzy:
Kajdanowicz, T.
Kazienko, P.
Powiązania:
https://bibliotekanauki.pl/articles/331286.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maszyna ucząca się
uczenie nadzorowane
metoda agregacji
struktura ramowa
machine learning
supervised learning
multilabel classification
error correcting output codes
ECOC
ensemble methods
binary relevance
framework
Opis:
A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 829-840
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Systematic Review of Ensemble Techniques for Software Defect and Change Prediction
Autorzy:
Khanna, Megha
Powiązania:
https://bibliotekanauki.pl/articles/2123249.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
ensemble learning
software change prediction
software defect prediction
software quality
systematic review
Opis:
Background: The use of ensemble techniques have steadily gained popularity in several software quality assurance activities. These aggregated classifiers have proven to be superior than their constituent base models. Though ensemble techniques have been widely used in key areas such as Software Defect Prediction (SDP) and Software Change Prediction (SCP), the current state-of-the-art concerning the use of these techniques needs scrutinization. Aim: The study aims to assess, evaluate and uncover possible research gaps with respect to the use of ensemble techniques in SDP and SCP. Method: This study conducts an extensive literature review of 77 primary studies on the basis of the category, application, rules of formulation, performance, and possible threats of the proposed/utilized ensemble techniques. Results: Ensemble techniques were primarily categorized on the basis of similarity, aggregation, relationship, diversity, and dependency of their base models. They were also found effective in several applications such as their use as a learning algorithm for developing SDP/SCP models and for addressing the class imbalance issue. Conclusion: The results of the review ascertain the need of more studies to propose, assess, validate, and compare various categories of ensemble techniques for diverse applications in SDP/SCP such as transfer learning and online learning.
Źródło:
e-Informatica Software Engineering Journal; 2022, 16, 1; art. no. 220105
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ECLogger: Cross-Project Catch-Block Logging Prediction Using Ensemble of Classifiers
Autorzy:
Lal, S.
Sardana, N.
Sureka, A.
Powiązania:
https://bibliotekanauki.pl/articles/953061.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
classification
debugging
ensemble logging
machine learning
source code
analysis
tracing
Opis:
Background: Software developers insert log statements in the source code to record program execution information. However, optimizing the number of log statements in the source code is challenging. Machine learning based within-project logging prediction tools, proposed in previous studies, may not be suitable for new or small software projects. For such software projects, we can use cross-project logging prediction. Aim: The aim of the study presented here is to investigate cross-project logging prediction methods and techniques. Method: The proposed method is ECLogger, which is a novel, ensemble-based, cross-project, catch-block logging prediction model. In the research We use 9 base classifiers were used and combined using ensemble techniques. The performance of ECLogger was evaluated on on three open-source Java projects: Tomcat, CloudStack and Hadoop. Results: ECLoggerBagging, ECLoggerAverageVote, and ECLoggerMajorityVote show a considerable improvement in the average Logged F-measure (LF) on 3, 5, and 4 source!target project pairs, respectively, compared to the baseline classifiers. ECLoggerAverageVote performs best and shows improvements of 3.12% (average LF) and 6.08% (average ACC – Accuracy). Conclusion: The classifier based on ensemble techniques, such as bagging, average vote, and majority vote outperforms the baseline classifier. Overall, the ECLoggerAverageVote model performs best. The results show that the CloudStack project is more generalizable than the other projects.
Źródło:
e-Informatica Software Engineering Journal; 2017, 11, 1; 7-38
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying a neural network ensemble to intrusion detection
Autorzy:
Ludwig, Simone A.
Powiązania:
https://bibliotekanauki.pl/articles/91620.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
ensemble learning
Deep Neural Networks
NSL-KDD data set
Opis:
An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 3; 177-178
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data
Autorzy:
Nayak, Sachin
Vincent, Shweta
Sumathi, K.
Kumar, Om Prakash
Pathan, Sameena
Powiązania:
https://bibliotekanauki.pl/articles/2055258.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
classification
Convolutional Neural Networks
Ensemble Learning
machine learning
metadata
Opis:
Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 251--257
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A machine learning approach for the segmentation of driving maneuvers and its application in autonomous parking
Autorzy:
Notomista, G.
Botsch, M.
Powiązania:
https://bibliotekanauki.pl/articles/91543.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
autonomous parking
ensemble learning
maneuver segmentation
Opis:
A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle–dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to excellent performance in parallel-, cross- and oblique-parking maneuvers. The work shows that segmentation using classifies and open-loop control are an efficient approach in autonomous driving for the implementation of driving maneuvers.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 4; 243-255
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 24

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