An Improvement Forward Floating Search Algorithm for Feature Selection in Power System Transient Stability Classification

被引：0

作者：

Ngoc Au Nguyen ^{[1
]}

Huy Anh Quyen ^{[1
]}

Trong Nghia Le ^{[1
]}

Thi Thanh Binh Phan ^{[1
]}

机构：

[1] HCM City Univ Technol & Educ, Ho Chi Minh City, Vietnam

来源：

AETA 2015: RECENT ADVANCES IN ELECTRICAL ENGINEERING AND RELATED SCIENCES | 2016年 / 371卷

关键词：

Classification/pattern recognition; Feature selection; Power system; Sequential forward selection;

D O I：

10.1007/978-3-319-27247-4_15

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposed feature selection algorithm in power system transient stability classification. Feature selection is a very important stage aimed at reducing a number of features, retaining distinctive features to reduce memory, increasing identification accuracy. The proven effectiveness of Improvement Forward Floating Search algorithm (IFFS) was compared with Sequential Forward Selection (SFS), Sequential Forward Floating Selection algorithm (SFFS) and Relief algorithm through analysis of results in power system transient stability classification using K-Nearest Neighbour (K-NN) on IEEE 30-bus standard power network. The analytical results showed that the IFFS achieved effective reduction features and recognition accuracy higher than other methods.

引用

页码：167 / 174

页数：8

共 50 条

[1] IMPROVED FORWARD FLOATING SELECTION ALGORITHM FOR FEATURE SUBSET SELECTION
Nakariyakul, Songyot
Casasent, David P.
[J]. PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION, VOLS 1 AND 2, 2008, : 793 - +
[2] A Forward Search Inspired Particle Swarm Optimization Algorithm for Feature Selection in Classification
Li, An-Da
Xue, Bing
Zhang, Mengjie
[J]. 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 786 - 793
[3] An improvement on floating search algorithms for feature subset selection
Nakariyakul, Songyot
Casasent, David P.
[J]. PATTERN RECOGNITION, 2009, 42 (09) : 1932 - 1940
[4] Analysis of Adaptive Floating Search Feature Selection Algorithm
Devakumari, D.
Thangavel, K.
[J]. COMPUTER NETWORKS AND INFORMATION TECHNOLOGIES, 2011, 142 : 526 - +
[5] A feed-forward artificial neural network with enhanced feature selection for power system transient stability assessment
Sawhney, Harinder
Jeyasurya, B.
[J]. ELECTRIC POWER SYSTEMS RESEARCH, 2006, 76 (12) : 1047 - 1054
[6] Feature selection and rule extraction for the estimation of power system transient stability
Guan, Lin
Wang, Lv
Wang Tong-Wen
[J]. 2007 CONFERENCE PROCEEDINGS IPEC, VOLS 1-3, 2007, : 389 - +
[7] A modified sequential deep floating search algorithm for feature selection
Lv, Jia
Peng, Qinke
Sun, Zhi
[J]. 2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, 2015, : 2988 - 2993
[8] Modified Floating Search Feature Selection Based on Genetic Algorithm
Homsapaya, Kanyanut
Sornil, Ohm
[J]. 3RD INTERNATIONAL CONFERENCE ON ELECTRICAL SYSTEMS, TECHNOLOGY AND INFORMATION (ICESTI 2017), 2018, 164
[9] An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems
Jeong, Young-Seon
Shin, Kyoung Seok
Jeong, Myong K.
[J]. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2015, 66 (04) : 529 - 538
[10] Constructive Lazy Wolf Search Algorithm for Feature Selection in Classification
David, H. Benjamin Fredrick
Suruliandi, A.
Raja, S. P.
[J]. INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2019, 28 (05)

← 1 2 3 4 5 →