The nature of statistical learning theory
The nature of statistical learning theory
Separated hierarchical decomposition of the Choquet integral
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Special issue on fuzzy measures and integrals in subjective evaluation
Data mining: concepts and techniques
Data mining: concepts and techniques
Fuzzy Measure Theory
A tutorial on support vector regression
Statistics and Computing
Classification with Choquet Integral with Respect to Signed Non-additive Measure
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Applying fuzzy measures and nonlinear integrals in data mining
Fuzzy Sets and Systems
Classification by nonlinear integral projections
IEEE Transactions on Fuzzy Systems
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Optimization-based classification approaches have well been used for decision making problems, such as classification in data mining. It considers that the contributions from all the attributes for the classification model equals to the joint individual contribution from each attribute. However, the impact from the interactions among attributes is ignored because of linearly or equally aggregation of attributes. Thus, we introduce the generalized Choquet integral with respect to the non-additive measure as the attributes aggregation tool to the optimization-based approaches in classification problem. Also, the boundary for classification is optimized in our proposed model compared with previous optimization-based models. The experimental result of two real life data sets shows the significant improvement of using the non-additive measure in data mining.