Classifying Continuous Data Set by ID3 Algorithm

This paper presents a modified version of the ID3 algorithm. The goal is to build the decision tree for classifying the continuous data set. An example in the training data set composes of some input features (attributes) and one predicate output. A proper feature ordering produces a shallow decision tree, which spends a logarithm time in classifying a data set. The original ID3 algorithm calculates the information gains of the features and arranges those features by descending order of the information gains. As a result, the decision tree selects a feature which has the biggest information gain at the top level. The algorithm repeats the feature ordering process for the rest of the features until there is not any unclassified example in the training data. However, one problem of the original ID3 algorithm is that it cannot classify the continuous feature in the data set. In order to serve a continuous feature, the ID3 algorithm is modified by quantizing the continuous feature into intervals and performing the classification process within those intervals. The modified algorithm is tested with a standard data set. The experimental results show a relationship between the number of intervals and the error rate on a standard real-world problem