Abstract
This paper proposes a genetic algorithm (GA) for error correcting output codes (ECOC). In our GA framework, the individual structure represents a solution for the multiclass problem, consisting of a codematrix and filtered feature subsets. Besides, the selection of base classifier is injected to form the second type of individual structure. A novel mutation operator is proposed to produce ECOC-compatible children in the evolutionary process, along with a set of legality checking schemes guaranteeing the legality of individuals. In addition, a local improvement function is implemented to optimize individuals, with the goal of further promoting their fitness value. To verify the performances of our algorithm, experiments are carried out with deploying some state-of-art ECOC algorithms. Results show that our GA with two different individual structures may perform diversely, but they can both lead to better results compared with other algorithms in most cases. Besides, the base learner selection embedded in GA leads to higher performance across different data sets.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (No. 61772023), and Natural Science Foundation of Fujian Province (No. 2016J01320).
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Zhang, YP., Liu, KH. (2019). A Novel Genetic Algorithm Approach to Improving Error Correction Output Coding. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_22
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DOI: https://doi.org/10.1007/978-3-030-29563-9_22
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