Feature selection: Comparative Analysis of Binary Metaheuristics and Population Based Algorithm with Adaptive Memory
- 作者: Hodashinsky I.1, Sarin K.1
-
隶属关系:
- Tomsk State University of Control Systems and Radio Electronics
- 期: 卷 45, 编号 5 (2019)
- 页面: 221-227
- 栏目: Article
- URL: https://journals.rcsi.science/0361-7688/article/view/176861
- DOI: https://doi.org/10.1134/S0361768819050037
- ID: 176861
如何引用文章
详细
The NP-hard feature selection problem is studied. For solving this problem, a population based algorithm that uses a combination of random and heuristic search is proposed. The solution is represented by a binary vector the dimension of which is determined by the number of features in the data set. New solution are generated randomly using the normal and uniform distribution. The heuristic underlying the proposed approach is formulated as follows: the chance of a feature to get into the next generation is proportional to the frequency with which this feature occurs in the best preceding solutions. The effectiveness of the proposed algorithm is checked on 18 known data sets. This algorithm is statistically compared with other similar algorithms.
作者简介
I. Hodashinsky
Tomsk State University of Control Systems and Radio Electronics
编辑信件的主要联系方式.
Email: hodashn@rambler.ru
俄罗斯联邦, Tomsk, 634050
K. Sarin
Tomsk State University of Control Systems and Radio Electronics
编辑信件的主要联系方式.
Email: sks@security.tomsk.ru
俄罗斯联邦, Tomsk, 634050