Data Mining Using Neural Networks in the form of Classification Rules: A Review
Manomita Chakraborty, Saroj Kr. Biswas, Biswajit Purkayastha · 2020
The increase in the rate of technological evolution is resulting in a reduction in the cost of various storage devices and as a consequence, enormous amounts of data are deposited from heterogeneous sources in raw forms. Therefore, some efficient data mining techniques are required that can process those data and retrieve useful information from them. Recently, machine learning algorithms are becoming very popular for doing various data mining tasks. Neural network is one of them, which has fascinated a lot of researchers due to its efficacy and fruitfulness in doing many tasks specially classification. But the main problem with neural network is its nature of black box, i.e., explaining the decision generated by a neural network is a daunting task. As a solution to this problem, rule extraction technique has been proposed which expresses the knowledge hidden in a learned network in the guise of understandable classification rules. The rule extraction is a very deep rooted technique and a very rich literature exists on this topic. However, a very less number of papers exist which mainly focused on surveying the existing literature. So, this work aims to provide a survey on the existing literature, and to shed light on some of the areas which needs to be focused to enrich the literature. At the same time the paper also tries to create a scope for the existing and the novice researchers to do research in this field.