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Multi-label classification learning concerns the determination of categories in the situation where one pattern may belong to more than one category. In this paper we propose a mixture approach, named FSMLKNN, which combines Fuzzy Similarity Measure (FSM) and Multi-Label K-Nearest Neighbor (MLKNN) for multi-label document classification. One of the problems associated with KNN-like approaches is its...
Multi-label document classification concerns the determination of categories in the situation where one document may belong to more than one category. In this paper we propose a fuzzy similarity-based approach for multi-label document classification. For a test document, the scores of its relevance to the classes are calculated based on a modified fuzzy similarity measure. The test document is then...
Credit scoring models have been widely studied in academic world and the business community. Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. The C4.5 is a learning algorithm which adopts local search strategy, it cannot obtain the best decision rules. On the other hand, the simulated...
This paper proposes a new approach to customer credit evaluation by synthesizing the rough set theory and decision tree theory. It adopts and improves a algorithm by Yuan Zhen, et al, (2005) while applying the rough set theory in attribute reduction. It also applies the C4.5 Algorithm proposed by Quinlan to build a decision tree model and adjusts relevant parameters during tree pruning period. Experimental...
C4.5 is a learning algorithm that adopts local search strategy, and it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a globally optimized algorithm and it avoids the drawbacks of C4.5. This paper proposes a new credit evaluation method based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed method...
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