Feature Selection for Classification through Population Random Search with Memory


如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

We propose a new approach for feature selection. The proposed approach is based on a combination of random and heuristic search strategies. The solution is represented as a binary vector whose dimension is determined by the number of features in the dataset. New solutions are generated at random using a normal and uniform distribution. The heuristic underlying the proposed approach can be formulated as follows: the chance of a feature to get into the next generation is proportional to the frequency of this feature appearing in previous best solutions. The proposed approach has been tested on several datasets from the KEEL repository. We also show an experimental comparison with other methods.

作者简介

I. Hodashinsky

Tomsk University of Control Systems and Radioelectronics

编辑信件的主要联系方式.
Email: hodashn@rambler.ru
俄罗斯联邦, Tomsk

K. Sarin

Tomsk University of Control Systems and Radioelectronics

编辑信件的主要联系方式.
Email: sks@security.tomsk.ru
俄罗斯联邦, Tomsk

补充文件

附件文件
动作
1. JATS XML

版权所有 © Pleiades Publishing, Inc., 2019