Performance comparison of Extreme Learning Machines and other machine learning methods on WBCD data set

Abstract

Breast cancer is one of the most common forms of cancer among women in our country and the world. Artificial intelligence studies are growing in order to reduce the mortality and early diagnosis needed for appropriate treatment. The Excessive Learning Machines (ELM) method, one of the machine learning approaches, is applied to the Wisconsin Breast Cancer Diagnostic (WBCD) dataset in this study, and the findings are compared to those of other machine learning methods. For this purpose, the same dataset is also classified using Multi-Layer Perceptron (MLP), Sequential Minimum Optimization (SMO), Decision Tree Learning (J48), Naive Bayes (NB), and K-Nearest Neighbor (KNN) methods. According to the results of the study, the ELM approach is more successful than other approaches on the WBCD dataset. It's also worth noting that as the number of neurons in the ELM grows, so does the learning ability of the network. However, after a certain number of neurons have passed, test performance begins to decline sharply. Finally, the ELM's performance is compared to the results of other studies in the literature.

Meteorological data analysis using extreme learning machines

“…of i-th neuron in hidden layer; i bshift of i-th neuron in hidden layer; s weight of s-th neuron in output layer. Recently, many algorithms have been proposed to modify and eliminate the shortcomings of classical ELMs[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”