Feature based classification of voice based biometric data through Machine learning algorithm

Abstract

In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected biometric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us benefits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.