Feature selection for monotonic classification via maximizing monotonic dependency

DOI

10.1080/18756891.2013.869903How to use a DOI?

Keywords

Monotonic classification, feature selection, fuzzy monotonic dependency

Abstract

Monotonic classification is a special task in machine learning and pattern recognition. As to monotonic classification, it is assumed that both features and decision are ordinal and there is the monotonicity constraints between the features and decision. Little work has been focused on feature selection for this type of tasks although a number of feature selection algorithms have been introduced for nominal classification problems. However these techniques can not be applied to monotonic classification as they do not consider the monotonicity constraints. In this work, we present a technique to compute the quality of features for monotonic classification. Using gradient directing search method, this method trains a feature weight vector by maximizing the fuzzy monotonic dependency, which was defined in fuzzy preference rough sets. We conduct some experiments to compare the classification performances of the proposed method with some other techniques. The experimental results show the effectiveness of the proposed algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Weiwei Pan
AU  - Qinghua Hu
AU  - Yanping Song
AU  - Daren Yu
PY  - 2014
DA  - 2014/06/01
TI  - Feature selection for monotonic classification via maximizing monotonic dependency
JO  - International Journal of Computational Intelligence Systems
SP  - 543
EP  - 555
VL  - 7
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.869903
DO  - 10.1080/18756891.2013.869903
ID  - Pan2014
ER  -