MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Automated extraction of extended structured motifs using multi-objective genetic algorithm
Expert Systems with Applications: An International Journal
Motif discovery using multi-objective genetic algorithm in biosequences
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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This paper introduces the optimized sequential pattern problem and presents a novel approach to find such patterns. All the methods described in the literature to optimize association rules employ a single objective measure, such as optimized confidence or optimized support. In this study, we propose a novel multi-objective Genetic Algorithm (GA) based optimization method for optimizing quantitative sequential patterns. The objective measures of are support, confidence and a parameter related to the total number of fuzzy sets in the sequence. Experimental results on a synthetic database demonstrate the effectiveness and applicability of the proposed method.