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Dynamic programming applied to rough sets attribute reduction

Abstract : Nowadays, and with the current progress in technologies and business sales, databases with large amount of data exist especially in retail companies. The main objective of this study is to reduce the complexity of the classifi cation problems while maintaining the prediction classifi cation quality. We propose to apply the promising technique Rough Set theory which is a new mathematical approach to data analysis based on classifi cation of objects of interest into similarity classes, which are indiscernible with respect to some features. Since some features are of high interest, this leads to the fundamental concept of "Attribute Reduction". The goal of Rough set is to enumerate good attribute subsets that have high dependence, discriminating index and signifi cance. The naïve way of is to generate all possible subsets of attribute but in high dimension cases, this approach is very ineff icient while it will require iterations. Therefore, we apply Dynamic programming technique in order to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. Implementation has been developed, applied, and tested over a 3 years historical business data in Retail Business. Simulations and visual analysis are shown and discussed in order to validate the accuracy of the proposed tool
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Soumis le : jeudi 9 janvier 2014 - 17:28:49
Dernière modification le : mardi 25 octobre 2022 - 11:58:11

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Walid Moudani, Ahmad Shahin, Fadi Shakik, Felix Mora-Camino. Dynamic programming applied to rough sets attribute reduction. Journal of Information & Optimization Sciences, 2013, 32 (6), pp 1371-1397. ⟨10.1080/02522667.2011.10700125⟩. ⟨hal-00926564⟩

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