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- W4366376922 abstract "Decision Tree (DT) induction is one of the popular data modelling techniques that commonly used in many real-world supervised learning problems. DT is a top-down recursive divide-and-conquer algorithm. It divides the datasets until all the instances of a sub-dataset belongs to the same class value. There are several advantages of DT e.g. it is easy to implement and easy to understand, it takes little prior knowledge, non-linear relationship don't affect the tree performance. DT can be used for both classification and regression tasks. The traditional DT methods use information gain, gain ratio, and gini values to select the root/best node. DT can be scalable to deal with Big Data e.g. RainForest and BOAT (Optimistic Decision Tree Construction) algorithms. In this paper, we have proposed a new method for decision tree construction where instead of using the traditional informative feature selection technique, we have considered each of the input feature to build decision tree and used these trees as an ensemble classifier. Each of the tree predicts the test data and returns the majority voting. We have tested the performance of proposed method with the well-known C4.5 classifier using datasets taken from UC Irvine Machine Learning Repository. The proposed method outperforms C4.5 classifier by almost 30%." @default.
- W4366376922 created "2023-04-21" @default.
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- W4366376922 date "2023-02-23" @default.
- W4366376922 modified "2023-09-25" @default.
- W4366376922 title "A New Method for Learning Decision Tree Classifier" @default.
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- W4366376922 doi "https://doi.org/10.1109/ecce57851.2023.10101557" @default.
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