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Wyświetlanie 1-15 z 34
Tytuł:
Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
Autorzy:
Ślot, K.
Adamiak, K.
Duch, P.
Żurek, D.
Powiązania:
https://bibliotekanauki.pl/articles/308598.pdf
Data publikacji:
2015
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
feature selection
kernel methods
pattern classification
Opis:
The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples. Several criteria that drive feature selection process are introduced and their performance is assessed and compared against the reference approach, which is a combination of kPCA and most expressive feature reordering based on the Fisher linear discriminant criterion. It has been shown that some of the proposed modifications result in generating feature spaces with noticeably better (at the level of approximately 4%) class discrimination properties.
Źródło:
Journal of Telecommunications and Information Technology; 2015, 2; 3-10
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data Stream Classification Using Classifier Ensemble
Autorzy:
Woźniak, Michał
Kasprzak, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/1373631.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:
data stream classification
classifier enslemble
pattern classification
forgetting
Opis:
For the contemporary business, the crucial factor is making smart decisions on the basis of the knowledge hidden in stored data. Unfortunately,m traditional simple methods of data analysis are not sufficient for efficient management of modern enterprizes, because they are not appropriate for the huge and growing amount of the data stored by them. Additionally data usually comes continuously in the form of so-called data stream. The great disadvantage of traditional classification methods is that they assume that statistical properties of the discovered concept are being unchanged, while in real situation, we could observe so-called concept drift, which could be caused by changes in the probabilities of classes or/and conditional probability distributions of classes. The potential for considering new training data is an important feature of machine learning methods used in security applications (spam filtering or intrusion detection) or decision support systems for marketing departments, which need to follow the changing client behavior. Unfortunately, the occurrence of concept drift dramatically decreases classification accuracy. This work presents the comprehensive study on the ensemble classifier approach applied to the problem of drifted data streams. Especially it reports the research on modifications of previously developed Weighted Aging Classifier Ensemble (WAE) algorithm, which is able to construct a valuable classifier ensemble for classification of incremental drifted stream data. We generalize WAE method and propose the general framework for this approach. Such framework can prune an classifier ensemble before or after assigning weights to individual classifiers. Additionally, we propose new classifier pruning criteria, weight calculation methods, and aging operators. We also propose rejuvenating operator, which is able to soften the aging effect, which could be useful, especially in the case if quite ”old” classifiers are high quality models, i.e., their presence increases ensemble accuracy, what could be found, e.g., in the case of recurring concept drift. The chosen characteristics of the proposed frameworks were evaluated on the basis of the wide range of computer experiments carried out on the two benchmark data streams. Obtained results confirmed the usability of proposed method to the data stream classification with the presence of incremental concept drift.
Źródło:
Schedae Informaticae; 2014, 23; 21-32
0860-0295
2083-8476
Pojawia się w:
Schedae Informaticae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Single spiking neuron multi-objective optimization for pattern classification
Autorzy:
Juarez-Santini, Carlos
Ornelas-Rodriguez, Manuel
Soria-Alcaraz, Jorge Alberto
Rojas-Domínguez, Alfonso
Puga-Soberanes, Hector J.
Espinal, Andrés
Rostro-Gonzalez, Horacio
Powiązania:
https://bibliotekanauki.pl/articles/385022.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
multi-objective optimization
spiking neuron
pattern classification
Opis:
As neuron models become more plausible, fewer computing units may be required to solve some problems; such as static pattern classification. Herein, this problem is solved by using a single spiking neuron with rate coding scheme. The spiking neuron is trained by a variant of Multi-objective Particle Swarm Optimization algorithm known as OMOPSO. There were carried out two kind of experiments: the first one deals with neuron trained by maximizing the inter distance of mean firing rates among classes and minimizing standard deviation of the intra firing rate of each class; the second one deals with dimension reduction of input vector besides of neuron training. The results of two kind of experiments are statistically analyzed and compared again a Mono-objective optimization version which uses a fitness function as a weighted sum of objectives.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 73-80
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effectiveness of active forgetting in machine learning applied to financial problems
Autorzy:
Nakayama, H.
Yoshii, K.
Powiązania:
https://bibliotekanauki.pl/articles/309245.pdf
Data publikacji:
2002
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
pattern classification
potential method
additional learning
forgetting
Opis:
One of main features in financial investment problems is that the situation changes very often over time. Under this circumstance, in particular, it has been observed that additional learning plays an effective role. However, since the rule for classification becomes more and more complex with only additional learning, some appropriate forgetting is also necessary. It seemms natural that many data are forgotten as the time elapses. On the other hand, it is expected more effective to forget unnecessary data actively. In this paper, several methods for active forgetting are suggested. The effectiveness of active forgetting is shown by examples in stock portfolio problems.
Źródło:
Journal of Telecommunications and Information Technology; 2002, 3; 24-29
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Geometryczna metoda selekcji informacji diagnostycznej
Geometrical method of the diagnostic information selection
Autorzy:
Dybała, J.
Radkowski, S.
Powiązania:
https://bibliotekanauki.pl/articles/328668.pdf
Data publikacji:
2004
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
selekcja informacji
klasyfikacja obrazów
information selection
pattern classification
Opis:
W pracy przedstawiono metodę selekcji cech stanu obiektu opartą na geometrii przestrzeni obserwacji. Zaprezentowana metoda selekcji informacji wykorzystuje dwa kryteria: zmodyfiko-wane kryterium Sebestyena oraz oryginalne kryterium liczby wzorców klas.
In the paper the feature selection method of object state is presented. This method is based on geometry of the observation space. The presented method of the information selection uses two criteria: the modified Sebestyen's criterion and original criterion of the prototypes classes number.
Źródło:
Diagnostyka; 2004, 30, T. 1; 159-162
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Discrete Fourier transform based pattern classifiers
Autorzy:
Hui, S.
Żak, S. H.
Powiązania:
https://bibliotekanauki.pl/articles/202074.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
pattern classification
multidimensional discrete Fourier transform
DFT
Fourier coefficients
Opis:
A technique for pattern classification using the Fourier transform combined with the nearest neighbor classifier is proposed. The multidimensional fast Fourier transform (FFT) is applied to the patterns in the data base. Then the magnitudes of the Fourier coefficients are sorted in descending order and the first P coefficients with largest magnitudes are selected, where P is a design parameter. These coefficients are then used in further processing rather than the original patterns. When a noisy pattern is presented for classification, the pattern’s P Fourier coefficients with largest magnitude are extracted. The coefficients are arranged in a vector in the descending order of their magnitudes. The obtained vector is referred to as the signature vector of the corresponding pattern. Then the distance between the signature vector of the pattern to be classified and the signature vectors of the patterns in the data base are computed and the pattern to be classified is matched with a pattern in the data base whose signature vector is closest to the signature vector of the pattern being classified.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 1; 15-22
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Correlation-based feature selection strategy in classification problems
Autorzy:
Michalak, K.
Kwaśnicka, H.
Powiązania:
https://bibliotekanauki.pl/articles/908379.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
selekcja cech
współzależność cech
klasyfikacja
feature selection
pairwise feature evaluation
feature correlation
pattern classification
Opis:
In classification problems, the issue of high dimensionality, of data is often considered important. To lower data dimensionality, feature selection methods are often employed. To select a set of features that will span a representation space that is as good as possible for the classification task, one must take into consideration possible interdependencies between the features. As a trade-off between the complexity of the selection process and the quality of the selected feature set, a pairwise selection strategy has been recently suggested. In this paper, a modified pairwise selection strategy is proposed. Our research suggests that computation time can be significantly lowered while maintaining the quality of the selected feature sets by using mixed univariate and bivariate feature evaluation based on the correlation between the features. This paper presents the comparison of the performance of our method with that of the unmodified pairwise selection strategy based on several well-known benchmark sets. Experimental results show that, in most cases, it is possible to lower computation time and that with high statistical significance the quality of the selected feature sets is not lower compared with those selected using the unmodified pairwise selection process.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2006, 16, 4; 503-511
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Critical exponent analysis applied to surface EMG signals for multifunction myoelectric control
Autorzy:
Phinyomark, A.
Phothisonothai, M.
Phukpattaranont, P.
Limsakul, C.
Powiązania:
https://bibliotekanauki.pl/articles/220544.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
biomedical signal processing
electromyography signal
feature extraction
fractal analysis
human machine interface
pattern classification
Opis:
Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855∼2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.
Źródło:
Metrology and Measurement Systems; 2011, 18, 4; 645-658
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Two Dimensional Wavelet Transform for Classification of Power Quality Disturbances
Autorzy:
Mollayi, N.
Mokhtari, H.
Powiązania:
https://bibliotekanauki.pl/articles/262752.pdf
Data publikacji:
2014
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
Tematy:
power quality
event detection and classification
two dimensional wavelet transform
pattern classification
image processing
feature
classifier system
Opis:
Identification of voltage and current disturbances is an important task in power system monitoring and protection. In this paper, the application of two-dimensional wavelet transform for characterization of a wide variety range of power quality disturbances is discussed, and a new algorithm, based on image processing techniques is proposed for this purpose. A matrix is formed based on a specified number of cycles in such a way that the samples of voltage signal in each cycle form one row of that matrix. This matrix can be regarded as a two dimensional image. A two-dimensional wavelet transform is used to decompose the image into approximation and details, which contain low frequency and high frequency components along the rows and columns, respectively. Different disturbances result into different special patterns in detail images. By processing the detail images, specific features are defined which can suitably discriminate various types of disturbances. Combination of the feature generation algorithm and a classifier system leads to a smart system for classification of wide variety range of disturbances.
Źródło:
Electrical Power Quality and Utilisation. Journal; 2014, 17, 2; 1-7
1896-4672
Pojawia się w:
Electrical Power Quality and Utilisation. Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pattern classification by spiking neural networks combining self-organized and reward-related spike-timing-dependent plasticity
Autorzy:
Nobukawa, Sou
Nishimura, Haruhiko
Yamanishi, Teruya
Powiązania:
https://bibliotekanauki.pl/articles/91886.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
spiking neural network
spike timing-dependent plasticity
dopamine-modulated spike timing-dependent plasticity
pattern classification
Opis:
Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopaminemodulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 4; 283-291
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie sieci neuronowych cp w wibroakustycznej diagnostyce uszkodzeń przekładni zębatej
Use of cp neural network in vibroacoustic diagnostics of toothed gears failure
Autorzy:
Dybała, J.
Radkowski, S.
Powiązania:
https://bibliotekanauki.pl/articles/328698.pdf
Data publikacji:
2004
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
selekcja cech
klasyfikacja obrazów
sieci neuronowe
diagnostyka wibroakustyczna
feature selection
pattern classification
neural networks
vibroacoustic diagnostic
Opis:
W artykule przedstawiono sposób zastosowywania neuronowego klasyfikatora zbudowanego na bazie sieci neuronowej z propagacją przeciwną w diagnostyce wibroakustycznej przekładni zębatej. Ponadto, w pracy przedstawiono unikalną metodę selekcji cech stanu obiektu opartą na geometrii przestrzeni obserwacji. W końcowej części artykułu przedstawiono jako przykład wyniki eksperymentu laboratoryjnego.
The article presents a way of applying a neural classifier constructed on the basis of counter-propagation neural network in vibroacoustic diagnostics of toothed gears. Moreover, in the paper the unique feature selection method of object state is presented. This method is based on geometry of the observation space. In final unit of article the results of laboratory experiment are presented as example.
Źródło:
Diagnostyka; 2004, 31; 59-66
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Handling class label noise in medical pattern classification systems
Autorzy:
Sáez, J. A.
Krawczyk, B.
Woźniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/333813.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
machine learning
pattern classification
class noise
noise filtering
decision support systems
uczenie maszynowe
klasyfikacja wzorców
filtracja zakłóceń
filtracja szumów
systemy wspomagania decyzji
Opis:
Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 123-130
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design of fuzzy rule-based classifiers through granulation and consolidation
Autorzy:
Riid, A.
Preden, J.-S.
Powiązania:
https://bibliotekanauki.pl/articles/91638.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
pattern recognition
fuzzy classification
complexity reduction
Opis:
This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 137-147
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions
Autorzy:
Ślipek, B.
Młynarczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/184708.pdf
Data publikacji:
2013
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
pattern recognition
automatic rock classification
image processing
Opis:
The paper presents the results of the automatic classification of rock images, taken under an optical microscope under different lighting conditions and with different polarization angles. The classification was conducted with the use of four pattern recognition methods: nearest neighbor, k-nearest neighbors, nearest mode, and optimal spherical neighborhoods on thin sections of five selected rocks. During research the CIELAB color space and the 9D feature space were used. The results indicate that changing both lighting conditions and polarization angles results in worsening the classification outcome, although not substantially. Duduring the automatic classification of rocks photographed under different lighting and polarization conditions, the highest number of correctly classified rocks (97%) is given by the nearest neighbor method. The results show that the automatic classification of rocks is possible within a predefined group of rocks. The results also indicate the optimal spherical neighborhoods method to be the safest method out of those tested, which means that it returns the lowest number of incorrect classifications.
Źródło:
Geology, Geophysics and Environment; 2013, 39, 4; 373-384
2299-8004
2353-0790
Pojawia się w:
Geology, Geophysics and Environment
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cognitive failure cluster enhancing the efficiency and the precision of the self-optimizing process model for bevel gear contact patterns
Autorzy:
Schmitt, R.
Niggemann, C.
Laass, M.
Powiązania:
https://bibliotekanauki.pl/articles/100025.pdf
Data publikacji:
2012
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
cognition
classification
self-optimization
gear testing
contact pattern
Opis:
The contact patterns of bevel gear sets are an important indicator for the acoustic quality of rear axle drives. The contact patterns are the result of complex interactions in the production process. This is due to many process steps, numerous influencing factors and interdependencies. In general, their effect on product variations is not fully comprehended. This impedes the design and control of the production process based on a holistic analytical model for new variants fulfilling the acoustic requirements. The approach with self-optimization is possible but can take a long time for the training of the artificial neural networks and the necessary iterations until a satisfying precision for the predicted process parameters is achieved. Also it can occur that the algorithm is not converging and therefore no satisfactory result is turned out at all. In this paper an approach is presented combining the flexibility of self-optimizing systems with the higher precision of delimited solution finders called the Cognitive Failure Cluster (CFC). The improvements provided by the clustering of the optimization program are evaluated regarding the training time and the precision of the result for a production lot of bevel gear sets.
Źródło:
Journal of Machine Engineering; 2012, 12, 1; 55-65
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 34

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