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Wyszukujesz frazę "feature reduction" wg kryterium: Temat

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    Wyświetlanie 1-13 z 13
    Tytuł:
    Finding robust transfer features for unsupervised domain adaptation
    Autorzy:
    Gao, Depeng
    Wu, Rui
    Liu, Jiafeng
    Fan, Xiaopeng
    Tang, Xianglong
    Powiązania:
    https://bibliotekanauki.pl/articles/331356.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    unsupervised domain adaptation
    feature reduction
    generalized eigenvalue decomposition
    object recognition
    adaptacja domeny
    redukcja cech
    rozkład wartości własnych
    rozpoznawanie obiektu
    Opis:
    An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 99-112
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement
    Opracowanie inteligentnego modelu dla rozpoznania konstrukcji kształtu bioder
    Autorzy:
    Jin, J.-F.
    Yang, Y.-C.
    Zou, F.-Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/234324.pdf
    Data publikacji:
    2016
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    intelligent recognition system
    probabilistic neural network
    classification accuracy
    feature reduction
    typical index
    cluster analysis
    inteligentny system rozpoznawania
    sieć neuronowa
    dokładność klasyfikacji
    funkcja redukcji
    typy kształtu bioder
    Opis:
    The purpose of this paper was to develop an intelligent recognition system consisting of a feature reduction method combining cluster and correlation analyses, and a probabilistic neural network (PNN) classifier to identify different types of hip shape from 3D measurement for each person. Firstly 28 items reflecting lower body part information of 300 female university students aging from 20 to 24 years were selected. The feature reduction method was employed to extract typical indices. Secondly hip shapes were subdivided into five types by a K-means cluster and analysis of variance (ANOVA). Finally the PNN was then trained to serve as a classifier for identifying five different hip shape types. The average classification accuracy of the scheme proposed was 97.37%, and its effectiveness was successfully validated by comparing with the BP and Support Vector Machine (SVM) scheme. Thus an intelligent recognition system was developed to make hip shape type classification of high-precision and time saving.
    Model łączy analizę skupień i korelacji oraz probabilistyczną sztuczną sieć neuronową dla identyfikacji różnych typów kształtów bioder opartą o pomiary 3D poszczególnych osób. Wyselekcjonowano 28 przypadków odzwierciedlających dolną część sylwetki 300 studentek w wieku od 20 do 24 lat. Zastosowano metodę redukcji poszczególnych właściwości dla wybrania typowych wskaźników. Następnie kształt bioder podzielono na 5 typów za pomocą algorytmu klastrowego i systemu ANOVA (analiza wariancji). Następnie przeprowadzono trening sieci neuronowej aby mogła posłużyć jako klasyfikator identyfikacji 5 różnych kształtów bioder. Przeciętna dokładność klasyfikacji proponowanego systemu wynosiła 97,37%, a efektywność była sukcesywnie sprawdzana przez porównanie schematów BP i SVM. W ten sposób stworzono inteligentny system rozpoznania typu kształtu bioder o dużej precyzji, pozwalający na oszczędność czasu.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2016, 5 (119); 110-118
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech emotion recognition based on sparse representation
    Autorzy:
    Yan, J.
    Wang, X.
    Gu, W.
    Ma, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/177778.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech emotion recognition
    sparse partial least squares regression SPLSR
    SPLSR
    feature selection and dimensionality reduction
    Opis:
    Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.
    Źródło:
    Archives of Acoustics; 2013, 38, 4; 465-470
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new low SNR underwater acoustic signal classification method based on intrinsic modal features maintaining dimensionality reduction
    Autorzy:
    Ju, Yang
    Wei, Zhengxian
    Li, Huangfu
    Feng, Xiao
    Powiązania:
    https://bibliotekanauki.pl/articles/259300.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    acoustic
    low SNR
    signal classification
    feature maintain
    dimension reduction
    Opis:
    The classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic. . This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the proces of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.
    Źródło:
    Polish Maritime Research; 2020, 2; 187-198
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems
    Autorzy:
    Kusy, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226697.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    probabilistic neural network
    dimensionality reduction
    feature selection
    feature extraction
    single decision tree
    random forest
    principal component analysis
    prediction ability
    Opis:
    This article presents the study regarding the problem of dimensionality reduction in training data sets used for classification tasks performed by the probabilistic neural network (PNN). Two methods for this purpose are proposed. The first solution is based on the feature selection approach where a single decision tree and a random forest algorithm are adopted to select data features. The second solution relies on applying the feature extraction procedure which utilizes the principal component analysis algorithm. Depending on the form of the smoothing parameter, different types of PNN models are explored. The prediction ability of PNNs trained on original and reduced data sets is determined with the use of a 10-fold cross validation procedure.
    Źródło:
    International Journal of Electronics and Telecommunications; 2015, 61, 3; 289-300
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Presenting a technique for registering images and range data using a topological representation of a path within an environment
    Autorzy:
    Ferreira, F.
    Davim, L.
    Rocha, R.
    Dias, J.
    Santos, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/385035.pdf
    Data publikacji:
    2007
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    sensor feature integration
    binary data
    Bernoulli mixture model
    dimensionality reduction
    robot localisation
    change detection
    Opis:
    This article presents a novel method to the utilize topological representation of a path, thatpath that is created from sequences of images from digital cameras and sensor data from range sensors. A topological representation of the environment is created by leading the robot around the environment during a familiarisation phaseLeading the robot around the environment during a familiarisation phase creates a topological representation of the environment. While moving down the same path, the robot is able to localise itself within the topological representation that is has been previously created. The principal contribution to the state of the art is that, by using a topological representation of the environment, individual 3D data sets acquired from a set of range sensors need not be registered in a single, [Global] Coordinate Reference System. Instead, 3D point clouds for small sections of the environment are indexed to a sequence of multi-sensor views, of images and range data. Such a registration procedure can be useful in the construction of 3D representations of large environments and in the detection of changes that might occur within these environments.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 3; 47-56
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Local embedding and dimensionality reduction in detection of skin tumor tissue
    Autorzy:
    Michalak, M.
    Świtoński, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333429.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie wzorców
    analiza wielospektralna
    redukcja wymiarowości
    selekcja cech
    pattern recognition
    multispectral analysis
    dimensionality reduction
    feature selection
    Opis:
    This article shows the limitation of the usage of dimensionality reduction methods. For this purpose three algorithms were analyzed on the real medical data. This data are multispectral images of human skin labeled as tumor or non-tumor regions. The classification of new data required the special algorithm of new data mapping that is also described in the paper. Unfortunately, the final conclusion is that this kind of local embedding algorithms should not be recommended for this kind of analysis and prediction.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 19; 59-65
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selection of the most important components from multispectral images for detection of tumor tissue
    Autorzy:
    Michalak, M.
    Świtoński, A.
    Stawarz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/951663.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie obrazów
    analiza wielospektralna
    obniżenie wymiarowości
    wybór funkcji
    pattern recognition
    multispectral analysis
    dimensionality reduction
    feature selection
    Opis:
    The problem raised in this article is the selection of the most important components from multispectral images for the purpose of skin tumor tissue detection. It occured that 21 channel spectrum makes it possible to separate healthy and tumor regions almost perfectly. The disadvantage of this method is the duration of single picture acquisition because this process requires to keep the device very stable. In the paper two approaches to the problem are presented: hill climbing strategy and some ranking methods.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 303-308
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligence in manufacturing systems: the pattern recognition perspective
    Autorzy:
    Zaremba, M. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/971032.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    Intelligent Manufacturing Systems
    pattern recognition
    computational intelligence
    neural networks
    distributed systems
    spatial filtering
    feature selection
    dimensionality reduction
    Opis:
    The field of Intelligent Manufacturing Systems (IMS) has been generally equated with the use of Artificial Intelligence and Computational Intelligence methods and techniques in the design and operation of manufacturing systems. Those methods and techniques are now applied in many different technological domains to deal with such pervasive problems as data imprecision and nonlinear system behavior. The focus in IMS is now shifting to a broader understanding of the intelligent behavior of manufacturing systems. The questions debated by researchers today relate more to what kind and what level of adaptability to instill in the structure and operation of a manufacturing system, with the discussions increasingly gravitating to the issue of system self-organization. This paper explores the changing face of IMS from the perspective of the pattern recognition domain. It presents design criteria for techniques that will allow us to implement manufacturing systems exhibiting adaptive and intelligent behaviour. Examples are given to show how incorporating pattern recognition capabilities can help us build more intelligence and self-organization into the manufacturing systems of the future.
    Źródło:
    Control and Cybernetics; 2010, 39, 1; 233-258
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm
    Autorzy:
    Kavi Priya, Santhosam
    Pon Karthika, Kasirajan
    Powiązania:
    https://bibliotekanauki.pl/articles/2201021.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    depression detection
    text classification
    dimensionality reduction
    hybrid feature selection
    wykrywanie depresji
    klasyfikacja tekstu
    redukcja wymiarowości
    wybór funkcji
    Opis:
    Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 117--131
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization on the complementation procedure towards efficient implementation of the index generation function
    Autorzy:
    Borowik, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/330597.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data reduction
    feature selection
    indiscernibility matrix
    logic synthesis
    index generation function
    redukcja danych
    selekcja cech
    synteza logiczna
    funkcja generowania indeksów
    Opis:
    In the era of big data, solutions are desired that would be capable of efficient data reduction. This paper presents a summary of research on an algorithm for complementation of a Boolean function which is fundamental for logic synthesis and data mining. Successively, the existing problems and their proposed solutions are examined, including the analysis of current implementations of the algorithm. Then, methods to speed up the computation process and efficient parallel implementation of the algorithm are shown; they include optimization of data representation, recursive decomposition, merging, and removal of redundant data. Besides the discussion of computational complexity, the paper compares the processing times of the proposed solution with those for the well-known analysis and data mining systems. Although the presented idea is focused on searching for all possible solutions, it can be restricted to finding just those of the smallest size. Both approaches are of great application potential, including proving mathematical theorems, logic synthesis, especially index generation functions, or data processing and mining such as feature selection, data discretization, rule generation, etc. The problem considered is NP-hard, and it is easy to point to examples that are not solvable within the expected amount of time. However, the solution allows the barrier of computations to be moved one step further. For example, the unique algorithm can calculate, as the only one at the moment, all minimal sets of features for few standard benchmarks. Unlike many existing methods, the algorithm additionally works with undetermined values. The result of this research is an easily extendable experimental software that is the fastest among the tested solutions and the data mining systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 803-815
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonparametric methods of supervised classification
    Autorzy:
    Jóźwik, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333226.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    pattern recognition
    feature selection
    k-NN rules
    pair-wise classifier
    artificial features
    linear classifier
    reference set size reduction
    rozpoznawanie wzorca
    wybór funkcji
    reguła k-NN
    sztuczne cechy
    klasyfikator liniowy
    Opis:
    Selected nonparametric methods of statistical pattern recognition are described. A part of them form modifications of the well known k-NN rule. To this group of the presented methods belong: a fuzzy k-NN rule, a pair-wise k-NN rule and a corrected k-NN rule. They can improve classification quality as compared with the standard k-NN rule. For the cases when these modifications would offer to large error rates an approach based on class areas determination is proposed. The idea of class areas can be also used for construction of the multistage classifier. A separate feature selection can be performed in each stage. The modifications of the k-NN rule and the methods based on determination class areas can be too slow in some applications, therefore algorithms for reference set reduction and condensation, for simple NN rule, are proposed. To construct fast classifiers it is worth to consider also a pair-wise linear classifiers. The presented idea can be used as in the case when the class pairs are linearly separable as well as in the contrary case.
    Źródło:
    Journal of Medical Informatics & Technologies; 2013, 22; 21-32
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment
    Autorzy:
    Ge, Wei
    Qi, Lin
    Tong, Lin
    Zhu, Jun
    Zhang, Jing
    Zhao, Dongyang
    Li, Ke
    Powiązania:
    https://bibliotekanauki.pl/articles/27311449.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    communication emitter identification
    feature extraction
    dimensionality reduction
    VMD
    ESDA
    variational mode decomposition
    exponential semi-supervised discriminant analysis
    identyfikacja emitera komunikacyjnego
    ekstrakcja cech
    redukcja wymiarowości
    rozkład w trybie wariacyjnym
    analiza dyskryminacyjna wykładnicza półnadzorowana
    Opis:
    The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 4; art. no. e145766
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

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