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Tytuł:
Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network
Autorzy:
Żelasko, Dariusz
Pławiak, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/1838182.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Pay&Require
ensemble learning
machine learning
resource allocation
QoS
uczenie zespołowe
uczenie maszynowe
alokacja zasobu
Opis:
Due to a continuous increase in the use of computer networks, it has become important to ensure the quality of data transmission over the network. The key issue in the quality assurance is the translation of parameters describing transmission quality to a certain rating scale. This article presents a technique that allows assessing transmission quality parameters. Thanks to the application of machine learning, it is easy to translate transmission quality parameters, i.e., delay, bandwidth, packet loss ratio and jitter, into a scale understandable by the end user. In this paper we propose six new ensembles of classifiers. Each classification algorithm is combined with preprocessing, cross-validation and genetic optimization. Most ensembles utilize several classification layers in which popular classifiers are used. For the purpose of the machine learning process, we have created a data set consisting of 100 samples described by four features, and the label which describes quality. Our previous research was conducted with respect to single classifiers. The results obtained now, in comparison with the previous ones, are satisfactory—high classification accuracy is reached, along with 94% sensitivity (overall accuracy) with 6/100 incorrect classifications. The suggested solution appears to be reliable and can be successfully applied in practice.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 135-153
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on the risk classification of cruise ship fires based on an attention-BP neural network
Autorzy:
Xiong, Zhenghua
Xiang, Bo
Chen, Ye
Chen, Bin
Powiązania:
https://bibliotekanauki.pl/articles/32912853.pdf
Data publikacji:
2022
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
cruise fire
simulation modeling
ensemble learning
BP neural network
Opis:
Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.
Źródło:
Polish Maritime Research; 2022, 3; 61-68
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
Autorzy:
Trostianchyn, A.M.
Izonin, I.V.
Duriagina, Z.A.
Tkachenko, R.O.
Kulyk, V.V.
Havrysh, B.M.
Powiązania:
https://bibliotekanauki.pl/articles/24200577.pdf
Data publikacji:
2022
Wydawca:
Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:
Sm-Co alloy
ensemble learning
gradient boosting
prediction accuracy
Stop Sm-Co
uczenie zespołowe
dokładność przewidywania
Opis:
Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
Źródło:
Archives of Materials Science and Engineering; 2022, 116, 2; 71--80
1897-2764
Pojawia się w:
Archives of Materials Science and Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes
Autorzy:
Topór, Tomasz
Sowiżdżał, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/27310145.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
machine learning
model stacking
ensemble method
carbonates
seismic attributes
porosity prediction
Opis:
This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.
Źródło:
Geology, Geophysics and Environment; 2023, 49, 3; 245--260
2299-8004
2353-0790
Pojawia się w:
Geology, Geophysics and Environment
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/1837996.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ensemble classifiers
machine learning
random forest
maximum likelihood
support vector machines
backpropagation
image classification
Opis:
The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
Źródło:
Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Designing Smart Antennas Using Machine Learning Algorithms
Autorzy:
Samantaray, Barsa
Das, Kunal Kumar
Roy, Jibendu Sekhar
Powiązania:
https://bibliotekanauki.pl/articles/27312957.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network
decision tree
ensemble algorithm
machine learning
smart antenna
support vector machine
Opis:
Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 4; 46--52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers
Autorzy:
Saini, Rashmi
Powiązania:
https://bibliotekanauki.pl/articles/2174656.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ensemble classifiers
Machine Learning
Random Forest
AdaBoost
vegetation mapping
vegetation indices
Opis:
Vegetation mapping is an active research area in the domain of remote sensing. This study proposes a methodology for the mapping of vegetation by integrating several vegetation indices along with original spectral bands. The Land Use Land Cover classification was performed by two powerful Machine Learning techniques, namely Random Forest and AdaBoost. The Random Forest algorithm works on the concept of building multiple decision trees for the final prediction. The other Machine Learning technique selected for the classification is AdaBoost (adaptive boosting), converts a set of weak learners into strong learners. Here, multispectral satellite data of Dehradun, India, was utilised. The results demonstrate an increase of 3.87% and 4.32% after inclusion of selected vegetation indices by Random Forest and AdaBoost respectively. An Overall Accuracy (OA) of 91.23% (kappa value of 0.89) and 88.59% (kappa value of 0.86) was obtained by means of the Random Forest and AdaBoost classifiers respectively. Although Random Forest achieved greater OA as compared to AdaBoost, interestingly AdaBoost provided better class-specific accuracy for the Shrubland class compared to Random Forest. Furthermore, this study also evaluated the importance of each individual feature used in the classification. Results demonstrated that the NDRE, GNDVI, and RTVIcore vegetation indices, and spectral bands (NIR, and Red-Edge), obtained higher importance scores.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 1; 57--74
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Podejście wielomodelowe w regresji danych symbolicznych interwałowych
Ensemble learning in regression model of symbolic interval data
Autorzy:
Pełka, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/424829.pdf
Data publikacji:
2014
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
ensemble learning
regression of symbolic data
interval-valued data
Opis:
Ensemble learning, which consist in using a lot of models (instead one single model) can be used in classical data analysis. The aim of the paper is to present an adaptation of ensemble learning with the use of bagging for regression analysis of symbolic interval-valued data. The article presents basic concepts concerning symbolic data analysis, the adaptation of ordinary least squares model for symbolic interval-valued data and the idea of bagging approach in ensemble learning. The empirical part contains the results of simulation studies obtained with the application of real and artificial data sets for centers and centers and range methods. The results show that both methods reach usually better results when using bagging than in case of a single model.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2014, 4(46); 211-220
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48
Autorzy:
Otoom, Mohammad Mahmood
Sattar, Khalid Nazim Abdul
Al Sadig, Mutasim
Powiązania:
https://bibliotekanauki.pl/articles/2201908.pdf
Data publikacji:
2023
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
cyber security
network intrusion
ensemble learning
machine learning
ML
Opis:
Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.
Źródło:
Advances in Science and Technology. Research Journal; 2023, 17, 2; 322--329
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
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
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:
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:
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:
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:
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
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