SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
Author:
Funder
ministry of higher education, malaysia
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s13748-021-00260-4.pdf
Reference59 articles.
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3. Geiß, C., Pelizari, P.A., Marconcini, M., Sengara, W., Edwards, M., Lakes, T., Taubenböck, H.: Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques. ISPRS J. Photogramm. Remote. Sens. 104, 175–188 (2015)
4. Yu, L., Zhou, R., Tang, L., Chen, R.: A dbn-based resampling svm ensemble learning paradigm for credit classification with imbalanced data. Appl. Soft Comput. 69, 192–202 (2018)
5. Lameski, P., Zdravevski, E., Mingov, R., Kulakov, A.: Svm parameter tuning with grid search and its impact on reduction of model over-fitting. In: Rough sets, fuzzy sets, data mining, and granular computing, pp. 464–474. Springer (2015)
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