Volume 7, Issue 3, June 2014, Pages 543 - 555
Feature selection for monotonic classification via maximizing monotonic dependency
Weiwei Pan, Qinghua Hu, Yanping Song, Daren Yu
Received 11 May 2012, Accepted 25 October 2013, Available Online 1 June 2014.
- https://doi.org/10.1080/18756891.2013.869903How to use a DOI?
- Monotonic classification, feature selection, fuzzy monotonic dependency
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Weiwei Pan AU - Qinghua Hu AU - Yanping Song AU - Daren Yu PY - 2014 DA - 2014/06 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 - https://doi.org/10.1080/18756891.2013.869903 ID - Pan2014 ER -