TY  - JOUR
EP  - 240
SP  - 201
AV  - public
VL  - 50
Y1  - 2018/08//
TI  - An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features
N1  - © Springer Science+Business Media Dordrecht 2017. This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
UR  - http://doi.org/10.1007/s10462-017-9541-y
ID  - discovery10045161
N2  - Hierarchical feature selection is a new research area in machine learning/data mining, which consists of performing feature selection by exploiting dependency relationships among hierarchically structured features. This paper evaluates four hierarchical feature selection methods, i.e., HIP, MR, SHSEL and GTD, used together with four types of lazy learning-based classifiers, i.e., Naïve Bayes, Tree Augmented Naïve Bayes, Bayesian Network Augmented Naïve Bayes and k-Nearest Neighbors classifiers. These four hierarchical feature selection methods are compared with each other and with a well-known ?flat? feature selection method, i.e., Correlation-based Feature Selection. The adopted bioinformatics datasets consist of aging-related genes used as instances and Gene Ontology terms used as hierarchical features. The experimental results reveal that the HIP (Select Hierarchical Information Preserving Features) method performs best overall, in terms of predictive accuracy and robustness when coping with data where the instances? classes have a substantially imbalanced distribution. This paper also reports a list of the Gene Ontology terms that were most often selected by the HIP method.
KW  - Hierarchical feature selection; Classification; Machine learning; Data mining; Bayesian classifiers; K-Nearest Neighbors; Biology of aging
A1  - Wan, C
A1  - Freitas, AA
JF  - Artificial Intelligence Review
ER  -