A nonlinear model to rank association rules based on semantic similarity and genetic network programing

Gao, Yang; Shimada, Kaoru; Mabu, Shingo; Hirasawa, Kotaro

doi:10.1002/tee.20385

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…As mentioned above, the conventional GNP‐based class association rule mining is one of the widely accepted state‐of‐art technologies in the field of class association rule mining [15–19,31–35]. There are other class association mining approaches, but they lack feasibility and practicality in the case of our study.…”

Section: Resultsmentioning

confidence: 99%

Acquisition of optimal attribute subset through genetic algorithm using GNP-based class association rule mining

Zhang

Shimada

Mabu

et al. 2014

IEEJ Trans Elec Electron Eng

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Attribute selection is a technique to prune less relevant information and discover high-quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset for the genetic network programming (GNP) based class association rule mining has been proposed, and this attribute selection process using genetic algorithm (GA) leads to a higher accuracy for classification. Class association rule mining through GNP is conducted with a small subset of data rather than the original large number of attributes; thus simple but important rules are obtained for classification while the local optimal problem is avoided. Simulation results with educational data show that the classification accuracy is largely improved from 52.73 to 74.54%, when classification is made using the optimal attribute subset.

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Section: Resultsmentioning

confidence: 99%

Acquisition of optimal attribute subset through genetic algorithm using GNP-based class association rule mining

Zhang

Shimada

Mabu

et al. 2014

IEEJ Trans Elec Electron Eng

Self Cite

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“…Each local predictability value is actually the prediction performance of that rule in one local test set which is generated by k-fold cross-validation. In 2008, Yang et al [40] suggested a personalized association rule ranking method based on Genetic Network Programming (GNP). The method learns a ranking measure which is a linear combination of semantic similarity and other well-known interestingness measures, includes support, confidence, lift and chi-squared.…”

Section: Related Workmentioning

confidence: 99%

MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification

2021

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A crucial characteristic of machine learning models in various domains (such as medical diagnosis, financial analysis, or real-time process monitoring) is the interpretability. The interpretation supports humans in understanding the meaning behind every single prediction made by the machine, and enables the user to assess trustworthiness before acting on the predictions. This article presents our work in building an interpretable classification model based on association rule mining and multi-objective optimization. The classification model itself is a rule list, making a single prediction based on multiple rules. The rule list consists of IF ... THEN statements that are understandable to humans. We choose these rules from a large set of pre-mined rules according to an interestingness measure which is formulated as a function of basic probabilities related to the rules. We learned the interestingness measure through multi-objective optimization, concentrating on two objectives: the classifier's size in terms of number of rules and prediction accuracy. The model is called MoMAC, "Multi-Objective optimization to combine Multiple Association rules into an interpretable Classification". The experimental results on benchmark datasets demonstrate that MoMAC outperforms other existing rule-based classification methods in terms of classification accuracy.

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“…The RuleRank model [3] is evolved by Genetic Network Programming. GNP is an extension of GP, which uses directed graphs as genes for evolutionary computation [4] [5], and evolves the graph structure with a predetermined number of nodes, so that it could be quite compact and efficient and never cause the bloat.…”

Section: Rulerank Modelmentioning

confidence: 99%

“…There has been some studies about combining different measures for ranking rules by evolutionary methods [3]. In this paper, we extend the method for associative classification.…”

Section: Introductionmentioning

confidence: 98%

Ranking association rules for classification based on genetic network programming

Gao

Mabu

Shimada

et al. 2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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In this paper, we propose a Genetic Network Programming (GNP) based ranking method to improve the accuracy of Classification Based on Association Rule(CBA). We start from an empirical phenomenon, that is, the accuracy could be improved by changing the ranking of rules in CBA. Then, we apply GNP to build a model, namely RuleRank, to find good ranking equations to rank association rules in CBA. The simulation results show that RuleRank could improve the accuracy of CBA effectively.

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A nonlinear model to rank association rules based on semantic similarity and genetic network programing

Cited by 8 publications

References 20 publications

Acquisition of optimal attribute subset through genetic algorithm using GNP-based class association rule mining

Acquisition of optimal attribute subset through genetic algorithm using GNP-based class association rule mining

MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification

Ranking association rules for classification based on genetic network programming

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