Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Dominance-Based Rough Set Approach Using Possibility and Necessity Measures
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
Form design of product image using grey relational analysis and neural network models
Computers and Operations Research
Application of elitist multi-objective genetic algorithm for classification rule generation
Applied Soft Computing
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
A dominance-based rough set approach to Kansei Engineering in product development
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An investigation into affective design using sorting technique and Kohonen self-organising map
Advances in Engineering Software
Autonomous classifiers with understandable rule using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
A Kansei mining system for affective design
Expert Systems with Applications: An International Journal
Variable precision Bayesian rough set model and its application to Kansei engineering
Transactions on Rough Sets V
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
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A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.