The Optimal Inference Rules Selection for Unstructured Data Multi-Classification
Abstract
The Fuzzy Inference System (FIS) is frequently utilized in a variety of Text Mining applications. In the text processing domains, where the amount of the processed data is vast, inserting manual rules for FIS remains a real issue, especially in the text processing domains, where the size of the processed databases is enormous. Therefore, an automated and optimal inference rules (IR) selection strengthens the FIS process. In this work, we propose to apply the FP-Growth as an association model algorithm and an automatic way to identify IR for fuzzy text vectorization. Once the fuzzy vectors are generated, we call the selection variables algorithms, e.g., Info Gain and Relief, to reduce the given descriptor dimensionality. To test the new descriptor performance, we propose multi-classes text classifification systems using several machine learning algorithms. Applying benchmarked databases, the new technique to produce Fuzzy descriptors achieves a signifificant gain in time, precision rules, and weighting quality. Moreover, comparing the classifification systems, the accuracy is improved by 10% comparing with other approaches.References
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