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Tytuł:
Propozycja agregowanego klasyfikatora kNN z selekcją zmiennych
The proposition of the kNN ensemble with feature selection.
Autorzy:
Kubus, Mariusz
Powiązania:
https://bibliotekanauki.pl/articles/424859.pdf
Data publikacji:
2016
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
k nearest neighbors
ensemble
feature selection
ReliefF algorithm
Opis:
Aggregated classification trees have gained recognition due to improved stability, and frequently reduced bias. However, the adaptation of this approach to the k nearest neighbors method (kNN), faces some difficulties: the relatively high stability of these classifiers, and an increase of misclassifications when the variables without discrimination power are present in the training set. In this paper we propose aggregated kNN classifier with feature selection. Its classification accuracy has been verified on the real data with added irrelevant variables.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2016, 3 (53); 32-41
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Metrics and similarities in modeling dependencies between continuous and nominal data
Autorzy:
Grabowski, M.
Korpusik, M.
Powiązania:
https://bibliotekanauki.pl/articles/91361.pdf
Data publikacji:
2013
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
k-nearest neighbors algorithm
data metrics
classification
continuous data
nominal data
Opis:
Classification theory analytical paradigm investigates continuous data only. When we deal with a mix of continuous and nominal attributes in data records, difficulties emerge. Usually, the analytical paradigm treats nominal attributes as continuous ones via numerical coding of nominal values (often a bit ad hoc). We propose a way of keeping nominal values within analytical paradigm with no pretending that nominal values are continuous. The core idea is that the information hidden in nominal values influences on metric (or on similarity function) between records of continuous and nominal data. Adaptation finds relevant parameters which influence metric between data records. Our approach works well for classifier induction algorithms where metric or similarity is generic, for instance k nearest neighbor algorithm or proposed here support of decision tree induction by similarity function between data. The k-nn algorithm working with continuous and nominal data behaves considerably better, when nominal values are processed by our approach. Algorithms of analytical paradigm using linear and probability machinery, like discriminant adaptive nearest-neighbor or Fisher’s linear discriminant analysis, cause some difficulties. We propose some possible ways to overcome these obstacles for adaptive nearest neighbor algorithm.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2013, 7, 10; 25-37
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study of Supervised Learning Methods for Malware Analysis
Autorzy:
Kruczkowski, M.
Niewiadomska-Szynkiewicz, E.
Powiązania:
https://bibliotekanauki.pl/articles/309481.pdf
Data publikacji:
2014
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
data classification
k-Nearest Neighbors
malware analysis
Naive Bayes
support vector machine (SVM)
Opis:
Malware is a software designed to disrupt or even damage computer system or do other unwanted actions. Nowadays, malware is a common threat of the World Wide Web. Anti-malware protection and intrusion detection can be significantly supported by a comprehensive and extensive analysis of data on the Web. The aim of such analysis is a classification of the collected data into two sets, i.e., normal and malicious data. In this paper the authors investigate the use of three supervised learning methods for data mining to support the malware detection. The results of applications of Support Vector Machine, Naive Bayes and k-Nearest Neighbors techniques to classification of the data taken from devices located in many units, organizations and monitoring systems serviced by CERT Poland are described. The performance of all methods is compared and discussed. The results of performed experiments show that the supervised learning algorithms method can be successfully used to computer data analysis, and can support computer emergency response teams in threats detection.
Źródło:
Journal of Telecommunications and Information Technology; 2014, 4; 24-33
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Hough transform in the classification process of inland ships
Autorzy:
Bobkowska, Katarzyna
Wawrzyniak, Natalia
Powiązania:
https://bibliotekanauki.pl/articles/135214.pdf
Data publikacji:
2019
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
Hough transform
k Nearest Neighbors (kNN)
image processing
classification
ship recognition
line detection
Opis:
This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2019, 58 (130); 9-15
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The PM-M prototype selection system
Autorzy:
Grudziński, K.
Powiązania:
https://bibliotekanauki.pl/articles/206602.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
selection of reference instances
prototype selection
k-Nearest Neighbors algorithm
classification of data
Opis:
In this paper, the algorithm, realizing the author’s prototype selection method, called PM-M (Partial Memory - Minimization) is described in details. Computational experiments that have been carried out with the raw PM-M model and with its majority ensembles indicate that even for the system, for which the average size of the selected prototype sets constitutes only about five percent of the size of the original training datasets, the obtained results of classification are still in a good statistical agreement with the 1-Nearest Neighbor (IB1) model which has been trained on the original (i.e. unpruned) data. It has also been shown that the system under study is competitive in terms of generalization ability with respect to other well established prototype selection systems, such as, for example, CHC, SSMA and GGA. Moreover, the proposed algorithm has shown approximately one to three orders of magnitude decrement of time requirements with respect to the necessary time, needed to complete the calculations, relative to the reference prototype classifiers, taken for comparison. It has also been demonstrated that the PM-M system can be directly applied to analysis of very large data unlike most other prototype methods, which have to rely on the stratification approach.
Źródło:
Control and Cybernetics; 2016, 45, 4; 539-561
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Curve skeleton extraction via k-nearest-neighbors based contraction
Autorzy:
Zhou, Jianling
Liu, Ji
Zhang, Min
Powiązania:
https://bibliotekanauki.pl/articles/331332.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
curve skeleton
points contraction
point cloud
k nearest neighbors
szkieletyzacja
chmura punktów
metoda k najbliższych sąsiadów
Opis:
We propose a skeletonization algorithm that is based on an iterative points contraction. We make an observation that the local center that is obtained via optimizing the sum of the distance to k nearest neighbors possesses good properties of robustness to noise and incomplete data. Based on such an observation, we devise a skeletonization algorithm that mainly consists of two stages: points contraction and skeleton nodes connection. Extensive experiments show that our method can work on raw scans of real-world objects and exhibits better robustness than the previous results in terms of extracting topology-preserving curve skeletons.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 123-132
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Instance based kNN modification for classification of medical data
Autorzy:
Orczyk, T.
Porwik, P.
Lewandowski, M.
Cholewa, M.
Powiązania:
https://bibliotekanauki.pl/articles/333353.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
k Nearest Neighbors
kNN
unbalanced data
incomplete data
algorytm najbliższych sąsiadów
dane niesymetryczne
dane niekompletne
Opis:
Paper describes a novel modification to a well known kNN algorithm, which enables using it for medical data, which often is a class-imbalanced data with randomly missing values. Paper presents the modified algorithm details, experiment setup, results obtained on a cross validated classification of a benchmark database with randomly removed values (missing data) and records (class imbalance), and their comparison with results of the state of the art classification algorithms.
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 99-106
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving prediction models applied in systems monitoring natural hazards and machinery
Autorzy:
Sikora, M.
Sikora, B.
Powiązania:
https://bibliotekanauki.pl/articles/331302.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
zagrożenie naturalne
szereg czasowy
k-najbliższy sąsiad
natural hazards monitoring
regression rules
time series forecasting
k-nearest neighbors
Opis:
A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 477-491
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection
Autorzy:
Wasilewska, Małgorzata
Bogucka, Hanna
Powiązania:
https://bibliotekanauki.pl/articles/226216.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
spectrum sensing
cognitive radio
machine learning
energy detection
4G
LTE
5G
k-nearest neighbors
random forest
Opis:
In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbor sand Random Forest show that these methods significantly improves the detection probability.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 1; 217-223
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of distributed denial of service attacks for IoT-based healthcare systems
Autorzy:
Kaur, Gaganjot
Gupta, Prinima
Powiązania:
https://bibliotekanauki.pl/articles/38701793.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
software defined networking
k-nearest neighbors
distributed denial of service
DPTCM-KNN approach
SVM
sieci definiowane programowo
k-najbliższy sąsiad
rozproszona odmowa usługi
Opis:
One of the major common assaults in the current Internet of things (IoT) network-based healthcare infrastructures is distributed denial of service (DDoS). The most challenging task in the current environment is to manage the creation of vast multimedia data from the IoT devices, which is difficult to be handled solely through the cloud. As the software defined networking (SDN) is still in its early stages, sampling-oriented measurement techniques used today in the IoT network produce low accuracy, increased memory usage, low attack detection, higher processing and network overheads. The aim of this research is to improve attack detection accuracy by using the DPTCM-KNN approach. The DPTCMKNN technique outperforms support vector machine (SVM), yet it still has to be improved. For healthcare systems, this work develops a unique approach for detecting DDoS assaults on SDN using DPTCM-KNN.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 167-186
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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