Parallel multi-objective genetic algorithms for associative classification rule mining

2011 ◽  
Author(s):  
B. S. P. Mishra ◽  
A. K. Addy ◽  
R. Roy ◽  
S. Dehuri
Keyword(s):  
Genetic Algorithms ◽  
Classification Rule ◽  
Associative Classification ◽  
Rule Mining ◽  
Multi Objective

scholarly journals Multi-objective rule mining using genetic algorithms

2004 ◽  
Vol 163 (1-3) ◽  
pp. 123-133 ◽  
Author(s):  
Ashish Ghosh ◽  
Bhabesh Nath
Keyword(s):  
Genetic Algorithms ◽  
Rule Mining ◽  
Multi Objective

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

scholarly journals ASSOCIATIVE CLASSIFICATION WITH MULTIOBJECTIVE TABU SEARCH

2020 ◽  
Vol 27 (2) ◽  
pp. 353-374
Author(s):  
RICARDO P. BEAUSOLEIL
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Evaluation Criteria ◽  
Classification Rule ◽  
Data Sets ◽  
Tabu Search Algorithm ◽  
Associative Classification ◽  
Rule Mining ◽  
Selection Rules ◽  
Rule Sets

This paper presents an application of Tabu Search algorithm to association rule mining. We focus our attention specifically on classification rule mining, often called associative classification, where the consequent part of each rule is a class label. Our approach is based on seek a rule set handled as an individual. A Tabu search algorithm is used to search for Pareto-optimal rule sets with respect to some evaluation criteria such as accuracy and complexity. We apply a called Apriori algorithm for an association rules mining and then a multiobjective tabu search to a selection rules. We report experimental results where the effect of our multiobjective selection rules is examined for some well-known benchmark data sets from the UCI machine learning repository.

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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