quote
LaTex
@ARTICLE{6381531,
author={B. Xue and M. Zhang and W. N. Browne},
journal={IEEE Transactions on Cybernetics},
title={Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach},
year={2013},
volume={43},
number={6},
pages={1656-1671},
keywords={Pareto optimisation;evolutionary computation;feature extraction;particle swarm optimisation;pattern classification;sorting;PSO-based multiobjective feature selection algorithms;Pareto front;classification performance;classification problems;crowding;dominance;evolutionary multiobjective algorithms;feature subsets;multiobjective particle swarm optimization;mutation;nondominated solutions;nondominated sorting;single objective feature selection method;two-stage feature selection algorithm;Error analysis;Heuristic algorithms;Optimization;Search problems;Standards;Support vector machines;Training;Feature selection;multi-objective optimization;particle swarm optimization (PSO);0},
doi={10.1109/TSMCB.2012.2227469},
ISSN={2168-2267},
month={Dec},}
Normal
B. Xue, M. Zhang and W. N. Browne, “Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach,” in IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1656-1671, Dec. 2013.
doi: 10.1109/TSMCB.2012.2227469
keywords: {Pareto optimisation;evolutionary computation;feature extraction;particle swarm optimisation;pattern classification;sorting;PSO-based multiobjective feature selection algorithms;Pareto front;classification performance;classification problems;crowding;dominance;evolutionary multiobjective algorithms;feature subsets;multiobjective particle swarm optimization;mutation;nondominated solutions;nondominated sorting;single objective feature selection method;two-stage feature selection algorithm;Error analysis;Heuristic algorithms;Optimization;Search problems;Standards;Support vector machines;Training;Feature selection;multi-objective optimization;particle swarm optimization (PSO);0},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6381531&isnumber=6670128
Summary
Irrelevant and redundant features
Feature selection
a small number of relevant features
- maximizing the classification performance
- minimizing the number of features
the first study on multi-objective particle swarm optimization (PSO) for feature selection
- nondominated sorting
- crowding, mutation, and dominance
compared with：
- two conventional feature selection methods
- a single objective feature selection method
- a two-stage feature selection algorithm
- three well-known evolutionary multi-objective algorithms
12 benchmark data sets
main content
PSO
FS
Traditional：
- SFS
- SBS
- the “plus-l-take away-r” method
- sequential forward floating selection (SFFS)
- sequential backward floating selection (SBFS)
- The Relief algorithm — assigns a weight to each feature
- Decision trees (DTs)
- The FOCUS algorithm — exhaustively
algorithm
TP, TN, FP, FN - true positives, true negatives, false positives and false negatives
ER — the error rate obtained by using all available features for classification on the training set
