Abstract
Imbalanced data will bring difficulties in data processing, which is very common in data engineering. These data usually have sophisticated distributions. Different resampling methods are required for dealing with data with different distributions, while fixed ones are adopted traditionally. Therefore, to select appropriate resampling methods for data with such characteristics, we propose a novel classification method for Imbalanced Data based on Ant Lion Optimizer, called ALOID. It combines adaptive resampling strategies, feature selection, and ensemble classifiers. The adaptive resampling strategy refers to utilizing roulette wheel selection to choose the most suitable resampling method with a greater probability for each dataset according to the variable probabilities of resampling methods. Then a two-stage approach is further used in feature selection: preprocessing and enhancing. In addition, we adopt an ensemble classifier with dynamic weights. The variable probabilities of resampling methods, features, and the weights of base classifiers are coded in individual solutions. A large number of comprehensive experiments have been carried out in this paper. ALOID is compared with 8 state-of-the-art algorithms on 33 publicly available imbalanced datasets. Using K-nearest neighbor as the base classifier, we have found ALOID outperforms other methods in most cases, especially on high-dimensional imbalanced datasets. Experiment results demonstrate the performance advantage of ALOID over other comparable algorithms.
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Li, M., Liu, Y., Zheng, Q., Li, X., Qin, W. (2022). A Classification Method for Imbalanced Data Based on Ant Lion Optimizer. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_26
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