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Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results
Author(s) -
Chen ChihWen,
Tsai YiHong,
Chang FangRong,
Lin WeiChao
Publication year - 2020
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12553
Subject(s) - feature selection , computer science , categorical variable , data mining , feature (linguistics) , filter (signal processing) , artificial intelligence , pattern recognition (psychology) , selection (genetic algorithm) , minimum redundancy feature selection , principal component analysis , dimensionality reduction , machine learning , philosophy , linguistics , computer vision
Abstract Feature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results. However, different feature selection algorithms use different criteria to select representative features, making it difficult to find the best algorithm for different domain datasets. The limitations of single feature selection methods can be overcome by the application of ensemble methods, combining multiple feature selection results. In the literature, feature selection algorithms are classified as filter, wrapper, or embedded techniques. However, to the best of our knowledge, there has been no study focusing on combining these three types of techniques to produce ensemble feature selection. Therefore, the aim here is to answer the question as to which combination of different types of feature selection algorithms offers the best performance for different types of medical data including categorical, numerical, and mixed data types. The experimental results show that a combination of filter (i.e., principal component analysis) and wrapper (i.e., genetic algorithms) techniques by the union method is a better choice, providing relatively high classification accuracy and a reasonably good feature reduction rate.