A new fitness function in genetic programming for classification of imbalanced data

Many real-world problems have an uneven distribution of data over different classes. The imbalanced nature of data impacts the performance of classifiers. The higher counts of majority class samples influence the learning abilities of well-known classifiers. Genetic programming (GP) algorithm based on natural evolution also impacts if the data's nature is imbalanced. The fitness function plays a pivotal role and impacts almost each building block of the GP framework. GP with the standard fitness function produces under-fitted and biased classifiers. Therefore, this paper has proposed a new fitness function in GP to classify the imbalanced data. The proposed method is used to classify nine imbalanced problems: ABL-18, ABL-9-18, BAL, YEAST2, YEAST1, ABL-9, ION, WDBC, and SPECT. The imbalanced factor of benchmark problems varies from 99:1 to 59:41. The proposed method's performance is compared with K-Nearest-Neighbourhood (KNN) and the standard fitness function-based GP methods. The GP with newly proposed fitness function gives average AUC values for ABL-18(99:1), ABL-9-18(94:6), BAL(92:8), YEAST2(89:11), YEAST1(84:16), ABL-9(83:17), ION(64:36), WDBC(63:37), and SPECT(59:41) as 0.714, 0.812, 0.975, 0.916, 0.768, 0.654, 0.872, 0.939, and 0.704, respectively, which are higher than KNN and the standard fitness function-based GP methods. The result outcomes prove the superiority of the proposed method.

Automated summary

The study examines the impact of imbalanced data on classifier performance and proposes a new fitness function for genetic programming to address imbalanced data classification. Experimental results show that the GP method with the new fitness function outperforms traditional methods and KNN in classifying imbalanced problems.