Abstract
Classification tasks are an integral part of science, industry, business, and health care systems; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this paper, motivated by learning styles in human brains, ANN's shortcomings are assuaged and its prediction power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. The proposed method, which we name Self-Organizing Error-Driven (SOED) Artificial Neural Network, shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five different datasets.
Highlights A synthesis of MLP and SOM is presented for tackling classification challenges. The superiority of SOED over MLP in addressing 5 classification tasks is presented. SOED is compared with other states of the art techniques such as DT, KNN, and SVM. It is shown that SOED is a more accurate and reliable in comparison with MLP. It is shown SOED is more accurate, reliable and transparent in comparison with MLP.
Model detecting learning styles with artificial neural network
