Feature Selection Based on a Novel Improved Tree Growth Algorithm

DOI

10.2991/ijcis.d.200219.001How to use a DOI?

Keywords

Feature selection; Tree growth algorithm; Evolutionary population dynamics; Metaheuristic

Abstract

Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of search ability in solving optimization of continuous problems. However, TGA cannot be directly applied to feature selection problems. Also, we find that its efficiency still leave room for improvement. To tackle this problem, in this study, a novel improved TGA (iTGA) is proposed, which can resolve the feature selection problem efficiently. The main contribution includes, (1) a binary TGA is proposed to tackle the feature selection problems, (2) a linearly increasing parameter tuning mechanism is proposed to tune the parameter in TGA, (3) the evolutionary population dynamics (EPD) strategy is applied to improve the exploration and exploitation capabilities of TGA, (4) the efficiency of iTGA is evaluated on fifteen UCI benchmark datasets, the comprehensive results indicate that iTGA can resolve feature selection problems efficiently. Furthermore, the results of comparative experiments also verify the superiority of iTGA compared with other state-of-the-art methods.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

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

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TY  - JOUR
AU  - Changkang Zhong
AU  - Yu Chen
AU  - Jian Peng
PY  - 2020
DA  - 2020/02/26
TI  - Feature Selection Based on a Novel Improved Tree Growth Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 247
EP  - 258
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200219.001
DO  - 10.2991/ijcis.d.200219.001
ID  - Zhong2020
ER  -