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.
Keywords
- Vertical partitioning
- Frequent itemsets
- Decision tree
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Guggari, S., Kadappa, V., Umadevi, V. (2020). Frequent Itemsets Based Partitioning Approach to Decision Tree Classifier. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_27
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DOI: https://doi.org/10.1007/978-3-030-66187-8_27
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