Paper Info
Title
Discrete Particle Swarm Optimization with local search strategy for Rule Classification
Abstract
Rule discovery is an important classification method that has been attracting a significant amount of researchers in recent years. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category. Besides the classical approaches, many rule mining approaches use biologically-inspired algorithms such as evolutionary algorithms and swarm intelligence approaches. In this paper, a Particle Swarm Optimization based discrete implementation with a local search strategy (DPSO-LS) was devised. The local search strategy helps to overcome local optima in order to improve the solution quality. Our DPSO-LS uses the Pittsburgh approach whereby a rule base is used to represent a `particle'. This rule base is evolved over time as to find the best possible classification model. Experimental results reveal that DPSO-LS outperforms other classification methods in most cases based on rule size, TP rates, FP rates, and precision.
Year
DOI
Venue
2012
10.1109/NaBIC.2012.6402256
NaBIC
Keywords
Field
DocType
rule classification method,discrete particle swarm optimization,knowledge based systems,rule mining,discrete implementation,local strategy,pattern classification,particle swarm optimisation,dpso-ls,pittsburgh approach,if-then rules,rule base,biologically-inspired algorithms,rule discovery,data mining,local search strategy,rule classification,rb,query formulation,particle swarm optimization
Particle swarm optimization,Data mining,Evolutionary algorithm,Local optimum,Computer science,Swarm intelligence,Knowledge-based systems,Multi-swarm optimization,Artificial intelligence,Local search (optimization),Machine learning,Metaheuristic
Conference
ISSN
ISBN
Citations 
2164-7364
978-1-4673-4767-9
4
PageRank 
References 
Authors
0.42
5
2
Name
Order
Citations
PageRank
Min Chen1162.06
Simone A Ludwig21309179.41