Temat: feature ranking - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Some Remarks on Feature Ranking Based Wrappers
Wybrane uwagi na temat podejścia wrappers bazującego na rankingu zmiennych
Autorzy:: Kubus, Mariusz
Powiązania:: https://bibliotekanauki.pl/articles/904802.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:: feature selection
wrappers
feature ranking
Opis:: One of the approaches to feature selection in discrimination or regression is learning models using various feature subsets and evaluating these subsets, basing on model quality criterion (so called wrappers). Heuristic or stochastic search techniques are applied for the choice of feature subsets. The most popular example is stepwise regression which applies hillclimbing. Alternative approach is that features are ranked according to some criterion and then nested models are learned and evaluated. The sophisticated tools of obtaining a feature rankings are tree based ensembles. In this paper we propose the competitive ranking which results in slightly lower classification error. In the empirical study metric and binary noisy variables will be considered. The comparison with a popular stepwise regression also will be given.
Jednym z podejść do problemu selekcji zmiennych w dyskryminacji lub regresji jest wykorzystanie kryterium oceny jakości modeli budowanych na różnych podzbiorach zmiennych (tzw. wrappers). Do wyboru podzbiorów zmiennych stosowane są techniki przeszukiwania (heurystyczne lub stochastyczne). Najpopularniejszym przykładem jest regresja krokowa wykorzystująca strategię wspinaczki. Alternatywne podejście polega na uporządkowaniu zmiennych wg wybranego kryterium, a następnie budowaniu modeli zagnieżdżonych i ich ocenie. Zaawansowanymi narzędziami budowy rankingów są agregowane drzewa klasyfikacyjne. W artykule został zaproponowany konkurujący ranking, który prowadzi do nieco mniejszych błędów klasyfikacji. W studium empirycznym rozważane są zmienne nieistotne metryczne oraz binarne. Przedstawiono też porównanie z popularną regresją krokową.
Źródło:: Acta Universitatis Lodziensis. Folia Oeconomica; 2013, 286
0208-6018
2353-7663
Pojawia się w:: Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Ensemble of data mining methods for gene ranking
Autorzy:: Wiliński, A.
Osowski, S.
Powiązania:: https://bibliotekanauki.pl/articles/201570.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: gene expression array
feature selection
gene ranking methods
classification
SVM
Opis:: The paper presents the ensemble of data mining methods for discovering the most important genes and gene sequences generated by the gene expression arrays, responsible for the recognition of a particular type of cancer. The analyzed methods include the correlation of the feature with a class, application of the statistical hypotheses, the Fisher measure of discrimination and application of the linear Support Vector Machine for characterization of the discrimination ability of the features. In the first step of ranking we apply each method individually, choosing the genes most often selected in the cross validation of the available data set. In the next step we combine the results of different selection methods together and once again choose the genes most frequently appearing in the selected sets. On the basis of this we form the final ranking of the genes. The most important genes form the input information delivered to the Support Vector Machine (SVM) classifier, responsible for the final recognition of tumor from non-tumor data. Different forms of checking the correctness of the proposed ranking procedure have been applied. The first one is relied on mapping the distribution of selected genes on the two-coordinate system formed by two most important principal components of the PCA transformation and applying the cluster quality measures. The other one depicts the results in the graphical form by presenting the gene expressions in the form of pixel intensity for the available data. The final confirmation of the quality of the proposed ranking method are the classification results of recognition of the cancer cases from the non-cancer (normal) ones, performed using the Gaussian kernel SVM. The results of selection of the most significant genes used by the SVM for recognition of the prostate cancer cases from normal cases have confirmed a good accuracy of results. The presented methodology is of potential use for practical application in bioinformatics.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 461-470
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: On the order equivalence relation of binary association measures
Autorzy:: Paradowski, M.
Powiązania:: https://bibliotekanauki.pl/articles/330881.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: association coefficient
result ranking
linear combination
zeroed variance determinant
feature selection
Opis:: Over a century of research has resulted in a set of more than a hundred binary association measures. Many of them share similar properties. An overview of binary association measures is presented, focused on their order equivalences. Association measures are grouped according to their relations. Transformations between these measures are shown, both formally and visually. A generalization coefficient is proposed, based on joint probability and marginal probabilities. Combining association measures is one of recent trends in computer science. Measures are combined in linear and nonlinear discrimination models, automated feature selection or construction. Knowledge about their relations is particularly important to avoid problems of meaningless results, zeroed generalized variances, the curse of dimensionality, or simply to save time.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 645-657
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: A weighted wrapper approach to feature selection
Autorzy:: Kusy, Maciej
Zajdel, Roman
Powiązania:: https://bibliotekanauki.pl/articles/2055180.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: feature selection
wrapper approach
feature significance
weighted combined ranking
convolutional neural network
classification accuracy
selekcja cech
sieć neuronowa konwolucyjna
dokładność klasyfikacji
Opis:: This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson’s linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion. In order to evaluate the effectiveness of the solution, the idea is compared with sequential feature selection methods that are widely known and used wrapper approaches. Additionally, to emphasize the need for dimensionality reduction, the results obtained on all attributes are shown. The verification of the outcomes is presented in the classification tasks of repository data sets that are characterized by a high dimensionality. The presented conclusions confirm that it is worth seeking new solutions that are able to provide a better classification result while reducing the number of input features.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 685--696
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Data mining methods for gene selection on the basis of gene expression arrays
Autorzy:: Muszyński, M.
Osowski, S.
Powiązania:: https://bibliotekanauki.pl/articles/329803.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: gene expression array
gene ranking
feature selection
clusterization measures
fusion
SVM classification
ekspresja genów
selekcja cech
klasyfikacja SVM
Opis:: The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected genes forming the required pattern, best associated with the cancerous cases. This resulting pattern of selected gene lists is treated as the input data to the classifier, performing the task of the final recognition of the patterns. The numerical results of the recognition of prostate cancer from normal (reference) cases using the selected genes and the support vector machine confirm the good performance of the proposed gene selection approach.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 657-668
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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