Improving the performance of association classifiers by rule prioritization

Chen, Chun‐Hao; Chiang, Rui‐Dong; Lee, Cho-Ming; Chen, Chih-Yang

doi:10.1016/j.knosys.2012.06.004

Cited by 11 publications

(1 citation statement)

References 18 publications

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“…Chen et al proposed a principal association mining (PAM) method to improve the accuracy and the size of classifier [4]. Some efficient methods were also proposed to improve the accuracy such as: using CBA to handle class imbalance [3] and uncertain datasets [10], methods that uses interestingness measures [11,27], a method that uses rule prioritization [5], and a method that uses closed sets [15].…”

Section: Mining Class Association Rulesmentioning

confidence: 99%

A Method for Query Top-K Rules from Class Association Rule Set

Nguyen

Nguyễn²,

et al. 2016

Intelligent Information and Database Systems

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Abstract. Methods for mining/querying Top-k frequent patterns and Top-k association rules have been developed in recent years. However, methods for mining/querying Top-k rules from a set of class association rules have not been developed. In this paper, we propose a method for querying Top-k class association rules based on the support. From the set of mined class association rules that satisfy the minimum support and minimum confidence thresholds, we use an insertion-based method to query Top-k rules. Firstly, we insert k rules from the rule set to the result set. After that, for each rule in the rest, we insert it into the result rule set using the idea of insertion strategy if its support is greater than the support of the last rule in the result rule set. Experimental results show that the proposed method is more efficient than obtaining the result after sorting the whole rule set.

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Section: Mining Class Association Rulesmentioning

confidence: 99%

A Method for Query Top-K Rules from Class Association Rule Set

Nguyen

Nguyễn²,

et al. 2016

Intelligent Information and Database Systems

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Studying Netconf in Hybrid Rule Ordering Strategies for Associative Classification

Hernández-León

Hernández-Palancar

Carrasco-Ochoa

et al. 2014

Lecture Notes in Computer Science

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In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy. Since this CAR list will be used to build a classifier, it is important to develop a good rule ordering strategy. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with Support-Confidence based rule ordering strategies. The fourth strategy, called Hybrid Specific Rules/Netconf (SR/NF), combines the Netconf measure with a rule ordering strategy based on the CAR's size. The experiments show that the proposed strategies obtain better classification accuracy than the best ordering strategies reported in the literature.

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