Abstract
This paper applies B-Cell algorithm (BCA) for credit scoring analysis problems. The proposed BCA-based method is combined with k-nearest neighbor (kNN) classifiers. In the algorithm, BCA is introduced to select the optimal feature subsets and kNNs are used to classify the investors in different groups representing different levels of credit in the classification phase. Experiments employing the benchmark data sets from UCI databases will be used to measure the performance of the algorithm. Its comparison with genetic algorithm, particle swarm optimization and ant colony optimization will be shown.
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Li, CA. (2013). Credit Scoring Analysis Using B-Cell Algorithm and K-Nearest Neighbor Classifiers* . In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_23
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DOI: https://doi.org/10.1007/978-3-642-38715-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
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