Temat: feature reduction - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

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

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