Abstract
A self-adaptive weighted extreme learning machine (SawELM) is proposed in this paper to deal with the imbalanced binary-class classification problems. SawELM calculates the output-layer weights based on a newly-designed self-adaptive mechanism which includes the following two modules: one is to gradually reduce the weights of wrongly-classified training instances and the other is to dynamically update the outputs of these wrongly-classified instances. On 50 imbalanced binary-class data sets selected from KEEL repository, we compare the accuracy, G-mean, and F-measure of SawELM with unweighted ELM (UnWELM) and weighted ELM (WELM). The experimental results show that the newly-designed self-adaptive mechanism is effective and SawELM obviously improves the imbalanced classification performance of WELM. SawWLM obtains the significantly higher G-mean and F-measure than UnWELM and WELM. Meanwhile, the accuracy of SawELM is better than WELM and comparable to UnWELM.
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The first author and second author contributed equally the same to this article which is supported by National Natural Science Foundations of China (61503252 and 61473194) and China Postdoctoral Science Foundation (2016T90799).
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Long, H., He, Y., Huang, J.Z., Wang, Q. (2017). Self-adaptive Weighted Extreme Learning Machine for Imbalanced Classification Problems. In: Kang, U., Lim, EP., Yu, J., Moon, YS. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10526. Springer, Cham. https://doi.org/10.1007/978-3-319-67274-8_11
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