A Decision tree is termed as good DT when it has small size and when new data is introduced it can be classified accurately. Pre-processing the input data is one of the good approaches for generating a good DT. When different data pre-processing methods are used with the combination of DT classifier it evaluates to give high performance. This paper involves the accuracy variation in the ID3 classifier when used in combination with different data pre-processing and feature selection method. The performances of DTs are produced from comparison of original and pre-processed input data and experimental results are shown by using standard decision tree algorithm-ID3 on a dataset.
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Author Name: Hemangi Bhalekar, Swati Kumbhar, Hiral Mewada, Pratibha Pokharkar, Shriya Patil,Mrs. Renuka Gound
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Keywords: Decision Tree, ID3, Feature selection