@article{90f20357-8d2d-4ab9-bd0b-39449906ef5d,
author = {Agnieszka Wosiak, Agata Dziomdziora},
title = {Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets},
journal = {Schedae Informaticae},
volume = {2015},
number = {Volume 24},
year = {2016},
issn = {1732-3916},
pages = {53-62},keywords = {dictionary learning; sparse representation; anomaly detection},
abstract = {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, Na¨ıve 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.},
doi = {10.4467/20838476SI.15.005.3027},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/feature-selection-and-classification-pairwise-combinations-for-high-dimensional-tumour-biomedical-datasets}
}