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- W3114252829 abstract "Decision tree is a classification technique which is widely used in many real world applications. It suffers from few challenges like structural instability, overfitting, curse of dimensionality etc. To address some of these issues, vertical partitioning paradigm is used in the literature. In vertical partitioning paradigm, the feature set is split into multiple subsets and these subsets are used for subsequent processing instead of original feature set. In this paper, we propose a novel partitioning approach using highest-size frequent itemsets. The efficiency of the method is evaluated using 5 standard datasets from UCI repository. The proposed method achieves significant improvement in classification accuracy and demonstrates better or competitive structural stability as compared to classical decision tree methods. The statistical significance of the results obtained by the proposed method is demonstrated using t-test, wilcoxon signed rank and pearson correlation tests." @default.
- W3114252829 created "2021-01-05" @default.
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- W3114252829 date "2020-01-01" @default.
- W3114252829 modified "2023-09-27" @default.
- W3114252829 title "Frequent Itemsets Based Partitioning Approach to Decision Tree Classifier" @default.
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- W3114252829 doi "https://doi.org/10.1007/978-3-030-66187-8_27" @default.
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