A genetic algorithm with entropy based initial bias for automated rule mining

Kapila,; Saroj, .; Kumar, Dinesh; Kanika, .

doi:10.1109/iccct.2010.5640477

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…After comparing the two generations of populations, best population is discovered. 4. After running several generations, final population can be realized.…”

Section: Proposed Genetic Algorithm With Modified Fitness Functionmentioning

confidence: 99%

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A Genetic Algorithm Approach using Improved Fitness Function for Classification Rule Mining

Salma-Tuz-Jakirin¹,

Ferdaus²,

Khan³

2014

IJCA

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Classification rule mining from huge amount of data is a challenging issue in data mining. Classification rules describe the relationship between predicting attributes and class label attribute and thus assign class label to unseen predicting attribute values. In this paper, a Genetic algorithm approach with modified fitness function for discovering classification rules has been presented. A flexible encoding scheme for representing a rule, genetic operators like crossover, mutation and also the stated fitness function with confidence, coverage, simplicity and interestingness properties have been exploited for discovering accurate, comprehensible and interesting rules. The results of proposed Genetic algorithm have been compared with existing J48, Jrip, Naive Bayesian algorithms. Experimental results endorse that the proposed algorithm produces relatively less number of classification rules with satisfactory accuracy rates.

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“…After comparing the two generations of populations, best population is discovered. 4. After running several generations, final population can be realized.…”

Section: Proposed Genetic Algorithm With Modified Fitness Functionmentioning

confidence: 99%

“…exist for classification. Genetic algorithms [1,2,3,4] is based on biological mechanism and takes attention of attribute interactions and has the capability of avoiding the convergence to local optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Approach using Improved Fitness Function for Classification Rule Mining

Salma-Tuz-Jakirin¹,

Ferdaus²,

Khan³

2014

IJCA

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“…Initial population is generated by methodically eliminating the randomness using a generalized uniform population method [11] or using entropy based initialization [9][10] [14]. Several approaches to encode prediction (IF-THEN) rules into the genome of individuals, as well as generalizing/specializing crossover and informationtheoretic rule pruning operator have been suggested [15].…”

Section: Introductionmentioning

confidence: 99%

“…As rules are generated from random POP, their accuracy will be poorer in comparison to the rules generated by a pop which has been initialized using domain knowledge. Therefore, many researchers addressed this problem by seeding the initial GA population by using the concepts like entropy and attribute significance [9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

Bottom-up Pittsburgh approach for discovery of classification rules

Sharma¹,

Ratnoo

2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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This paper presents bottom-up Pittsburgh approach for discovery of classification rules. Population initialization makes use of entropy as the attribute significance measure and contains variable sized organizations. Each organization contains a set of IF-THEN rules. As bottom-up approach is employed, so traditional operators are not feasible and efficient to use. Therefore, four evolutionary operators are devised for realizing the evolutionary operations performed on organizations. Bottomup Pittsburgh approach gives best set of rule having good accuracy. In experiments, the effectiveness of the proposed algorithm is evaluated by comparing the results of bottom-up Pittsburgh with and without entropy to the top-down Michigan approach with and without entropy on 10 datasets from the UCI and KEEL repository.All results show that bottom-up Pittsburgh approach achieves a higher predictive accuracy and is more consistent.

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“…A fixed encoding scheme is applied to the chromosomes and specific design is used for the mutation operator for GA designed by Fidelis et al [10] to discover comprehensible classification rules. A GA with entropy based filtering bias to initial population for automated rule mining proposed by Kapila et al [11]. A classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of censored production rules proposed by Bharadwaj & Al-Maqaleh [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for Discovering Classification Rules in Data Mining

Al-Maqaleh¹,

Shahbazkia²

2012

IJCA

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Data mining has as goal to discover knowledge from huge volume of data. Rule mining is considered as one of the usable mining method in order to obtain valuable knowledge from stored data on database systems. In this paper, a genetic algorithm-based approach for mining classification rules from large database is presented. For emphasizing on accuracy, coverage and comprehensibility of the rules and simplifying the implementation of a genetic algorithm. The design of encoding, genetic operators and fitness function of genetic algorithm for this task are discussed. Experimental results show that genetic algorithm proposed in this paper is suitable for classification rule mining and those rules discovered by the algorithm have higher classification performance to unknown data.

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A genetic algorithm with entropy based initial bias for automated rule mining

Cited by 7 publications

References 17 publications

A Genetic Algorithm Approach using Improved Fitness Function for Classification Rule Mining

A Genetic Algorithm Approach using Improved Fitness Function for Classification Rule Mining

Bottom-up Pittsburgh approach for discovery of classification rules

A Genetic Algorithm for Discovering Classification Rules in Data Mining

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