Evolutionary Multi-objective Rule Selection for Classification Rule Mining

2008 ◽  
pp. 47-70 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Isao Kuwajima ◽  
Yusuke Nojima
Keyword(s):  
Classification Rule ◽  
Rule Mining ◽  
Multi Objective ◽  
Rule Selection ◽  
Selection For

IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

2021 ◽  
Vol 20 (01) ◽  
pp. 2150010
Author(s):  
Parashu Ram Pal ◽  
Pankaj Pathak ◽  
Shkurte Luma-Osmani
Keyword(s):  
Association Rules ◽  
Search Space ◽  
Classification Rule ◽  
Rule Generation ◽  
Classification Methods ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Selection ◽  
Speed Up ◽  
Experimental Findings

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets

2015 ◽  
Vol 34 ◽  
pp. 705-720 ◽  
Author(s):  
Julie Jacques ◽  
Julien Taillard ◽  
David Delerue ◽  
Clarisse Dhaenens ◽  
Laetitia Jourdan
Keyword(s):  
Local Search ◽  
Imbalanced Data ◽  
Classification Rule ◽  
Data Sets ◽  
Rule Mining ◽  
Imbalanced Data Sets ◽  
Multi Objective

A New Approach to Associative Classification Based on Binary Multi-Objective Particle Swarm Optimization

2012 ◽  
pp. 230-252
Author(s):  
Madhabananda Das ◽  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Extraction ◽  
Classification Rules ◽  
Associative Classification ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Rule Selection

Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multi-objective problem rather than a single objective one. As the problem is a discrete combinatorial optimization problem, it was necessary to develop a binary multi-objective particle swarm optimization (BMOPSO) to optimize the measure like coverage and confidence of association rule mining (ARM) to extract classification rules in rule extraction phase. In rule selection phase, a small number of rules are targeted from the extracted rules by BMOPSO to design an accurate and compact classifier which can maximize the accuracy of the rule sets and minimize their complexity simultaneously. Experiments are conducted on some of the University of California, Irvine (UCI) repository datasets. The comparative result of the proposed method with other standard classifiers confirms that the new proposed approach can be a suitable method for classification.

A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization

2011 ◽  
Vol 2 (2) ◽  
pp. 51-73 ◽  
Author(s):  
Madhabananda Das ◽  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Extraction ◽  
Classification Rules ◽  
Associative Classification ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Rule Selection

Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multi-objective problem rather than a single objective one. As the problem is a discrete combinatorial optimization problem, it was necessary to develop a binary multi-objective particle swarm optimization (BMOPSO) to optimize the measure like coverage and confidence of association rule mining (ARM) to extract classification rules in rule extraction phase. In rule selection phase, a small number of rules are targeted from the extracted rules by BMOPSO to design an accurate and compact classifier which can maximize the accuracy of the rule sets and minimize their complexity simultaneously. Experiments are conducted on some of the University of California, Irvine (UCI) repository datasets. The comparative result of the proposed method with other standard classifiers confirms that the new proposed approach can be a suitable method for classification.

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