Abstract
Data mining is the process of analyzing data from a different category. This data provide information and data mining will extracts a new knowledge from it and a new useful information is created. Decision tree learning is a method commonly used in data mining. The decision tree is a model of decision that looklike as a tree-like graph with nodes, branches and leaves. Each internal node denotes a test on an attribute and each branch represents the outcome of the test. The leaf node which is the last node will holds a class label. Decision tree classifies the instance and helps in making a prediction of the data used. This study focused on a J48 algorithm for classifying a gender by using fingerprint features. There are four types of features in the fingerprint that is used in this study, which is Ridge Count (RC), Ridge Density (RD), Ridge Thickness to Valley Thickness Ratio (RTVTR) and White Lines Count (WLC). Different cases have been determined to be executed with the J48 algorithm and a comparison of the knowledge gain from each test is shown. All the result of this experiment is running using Weka and the result achieve 96.28% for the classification rate.
Highlights
A decision tree is a graph that uses a branching method to illustrate every possible outcome of the decision
The univariate decision tree is a decision node which considers only one feature that leads to the axis splits while the multivariate decision tree is a decision nodes that divide the input space into two widths an arbitrary hyperplane and leading to an oblique splits [4]
A J48 algorithm is an extension of an ID3 algorithm which is from the univariate decision trees
Summary
A decision tree is a graph that uses a branching method to illustrate every possible outcome of the decision. Verma et al [7] used Support Vector Machine (SVM) as a classifier for fingerprint-based gender classification problem. Abdullah et al [12][13] used several popular classifier for classification such as Multilayer Perceptron Neural Network (MLPNN), Support Vector Machine (SVM), Bayes Net and kNearest Neighbor (kNN) in classifying gender using the fingerprint features. They achieved above 95% of overall classification rate using 10-fold cross validation test. This study aims to see the performance of the J48 algorithm on fingerprint-based gender classification where J48 is commonly used in classification problem for the univariate decision trees.
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