Efficient Associative Classification using Genetic Network Programming

Ibrahim, S. P. Syed; Chandran, Karthik

doi:10.5120/3572-4929

Cited by 7 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Apriori rule generation method and genetic algorithm were respectively used for generating the work rules and for selecting randomly a subset of rules [5].In this paper a statistical approach is used to prune the less significant rules [11].…”

Section: Rule Pruningmentioning

confidence: 99%

Integrated Associative Classification and Neural Network Model Enhanced by Using a Statistical Approach

Mathew¹

2013

IJDKP

View full text Add to dashboard Cite

Association rules is a novel data mining techniquethat has been mainly used for data description,exploration and prediction in knowledge discovery and decision support systems.The association rulemining algorithm is modified to handle the user-defined input constraints. Associative classificationisprovided with a large number of rules, from which aset of quality rules are chosen to develop an efficientclassifier. Many attribute selection measures are used to reduce the number of generated rules. In thispaper the pruning of rule sets is facilitated by chi squared analysis thereby only positively correlated rulesare used in the classifier. Also the Neural NetworkAssociative Classification system is used in ordertoimprove the accuracy of the classifier. The trainednetwork is then used to classify the future data.Theperformance of the Neural Network Associative Classification system is analyzed with the datasets fromUCI machine learning repository

show abstract

Section: Rule Pruningmentioning

confidence: 99%

Integrated Associative Classification and Neural Network Model Enhanced by Using a Statistical Approach

Mathew¹

2013

IJDKP

View full text Add to dashboard Cite

show abstract

“…In [19] the author's proposed genetic network based associative classification method, which generates sufficient number of rules to construct the classifier. Here information gain attribute is used to construct the compact genetic network.…”

Section: Associative Classificationmentioning

confidence: 99%

“…To improve the performance, recently the authors proposed compact weighted associative classification (CWAC) [19]. The CWAC algorithm is completely varies from WAC.…”

Section: Introductionmentioning

confidence: 99%

Compact Weighted Class Association Rule Mining Using Information Gain

Ibrahim¹,

Chandran²

2011

IJDKP

View full text Add to dashboard Cite

Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed associative classification algorithm chooses one non class informative attribute from dataset and all the weighted class association rules are generated based on that attribute. The weight of the item is considered as one of the parameter in generating the weighted class association rules. This proposed algorithm calculates the weight using the HITS model. Experimental results show that the proposed system generates less number of high quality rules which improves the classification accuracy.

show abstract

“…Based on this idea the authors of [9] and [10] proposed information gain based weighted associative classification methods. In [11] the authors proposed associative classification method using genetic network programming. Here information gain attribute is used to construct the initial genetic network.…”

Section: Introductionmentioning

confidence: 99%

“…Here information gain attribute is used to construct the initial genetic network. The method proposed in [7] - [11] shows that, Information gain based approach reduces the computation cost.…”

Section: Introductionmentioning

confidence: 99%

LACI: Lazy Associative Classification using Information Gain

Ibrahim¹,

Chandran²,

Kanthasamy³

2012

IJET

View full text Add to dashboard Cite

Associative classification method applies association rule mining technique in classification and achieves higher classification accuracy. However, it is a known fact that associative classification typically yields a large number of rules, from which a set of high quality rules are chosen to construct an efficient classifier. Hence, generating, ranking and selecting a small subset of high-quality rules without jeopardizing the classification accuracy is of prime importance but indeed a challenging task. Lazy learning associative classification method eliminates the need of constructing the classifier but suffers with high computation cost. This paper proposes lazy associative classification using Information gain where, the system first chooses the Information gained attribute from the training sample and computes highest subset probability and then it directly predicts the class label. This proposed method not only reduces the computation cost but also improves the classification accuracy. Experimental result shows that the proposed system outperforms the traditional associative classification methods and the existing lazy associative classification method.

show abstract

Efficient Associative Classification using Genetic Network Programming

Cited by 7 publications

References 17 publications

Integrated Associative Classification and Neural Network Model Enhanced by Using a Statistical Approach

Integrated Associative Classification and Neural Network Model Enhanced by Using a Statistical Approach

Compact Weighted Class Association Rule Mining Using Information Gain

LACI: Lazy Associative Classification using Information Gain

Contact Info

Product

Resources

About