引用
LaTex
@article{HANCER2018462,
title = “Pareto front feature selection based on artificial bee colony optimization”,
journal = “Information Sciences”,
volume = “422”,
pages = “462 - 479”,
year = “2018”,
issn = “0020-0255”,
doi = “https://doi.org/10.1016/j.ins.2017.09.028“,
url = “http://www.sciencedirect.com/science/article/pii/S0020025516312609“,
author = “Emrah Hancer and Bing Xue and Mengjie Zhang and Dervis Karaboga and Bahriye Akay”,
keywords = “Feature selection, Classification, Multi-objective optimization, Artificial bee colony”
}
Normal
Emrah Hancer, Bing Xue, Mengjie Zhang, Dervis Karaboga, Bahriye Akay,
Pareto front feature selection based on artificial bee colony optimization,
Information Sciences,
Volume 422,
2018,
Pages 462-479,
ISSN 0020-0255,
https://doi.org/10.1016/j.ins.2017.09.028.
(http://www.sciencedirect.com/science/article/pii/S0020025516312609)
Keywords: Feature selection; Classification; Multi-objective optimization; Artificial bee colony
摘要
to maximize the classification performance
to minimize the number of selected features
the curse of dimensionality
a multi-objective problem
multi-objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators
ABC with binary representation and ABC with continuous representation
12 benchmark datasets
linear forward selection, greedy stepwise backward selection, two single objective ABC algorithms and three well-known multi-objective evolutionary computation algorithms
主要内容
ABC
Non-EC approaches
wrapper：
- sequential forward selection (SFS)
- sequential backward selection (SBS)
- sequential forward floating selection (SFFS)
- sequential backward floating selection (SFBS)
filter：
- FOCUS — exhaustive search
- Relief
- information theoretic approaches： MIFS, mRmR and MIFS-U
提出的算法
objectives:
- (1) minimizing the feature subset size
- (2) maximizing the classification accuracy
multi-objective ABC based wrapper approach
Bin-MOABC and Num-MOABC
A calculate probabilities for onlookers:
B update individuals:
greedy selection
a fast non-dominated sorting scheme
C generate new individuals:
binary solution generator (BSG)：
numeric solution generator (NSG))：
D Representation and fitness function:
activation codes — 【0，1】
vary / discrete
a user specified threshold value — 0.5、
The classification error rate of a feature subset：
试验
UCI machine learning repository
加权和
ER — the error rate calculated through all available features in the dataset
NSGAII, NSSABC and multi-objective PSO (MOPSO)
