DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance. However, training decision trees can be computationally expensive, particularly for large and high-dimensional datasets, largely due to the exhaustive search over candidate splits at each node. To improve computational efficiency, we propose Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors. By incorporating class-aware structure, DICS significantly reduces th
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- LinkedLinked via arxiv author · 85%MD Saifur Rahman Mazumder →
“DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers”
- LinkedLinked via arxiv author · 85%Yufeng Yuan →
“DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers”