- Tytuł:
- Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets
- Autorzy:
- Wosiak, Agnieszka
- Powiązania:
- https://bibliotekanauki.pl/articles/1373672.pdf
- Data publikacji:
- 2015
- Wydawca:
- Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
- Tematy:
-
feature selection
classification
high-dimensional tumour biomedical datasets - Opis:
- This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Naive Bayes, k-nearest neighbours and sequential minimal optimization algorithm for training support vector machines, as well as seven attribute selection methods: Correlation-based Feature Selection, chi-squared, information gain, gain ratio, symmetrical uncertainty, ReliefF and SVM-RFE (Support Vector Machine-Recursive Feature Elimination). In this paper, SVMRFE feature selection technique combined with SMO classifier has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.
- Źródło:
-
Schedae Informaticae; 2015, 24; 53-62
0860-0295
2083-8476 - Pojawia się w:
- Schedae Informaticae
- Dostawca treści:
- Biblioteka Nauki
Artykuł