This advanced research book introduces well-designed improvements to major machine learning algorithms, including decision trees, KNN, random forests, and clustering methods. The book focuses on reducing model complexity while improving accuracy, robustness, and execution time through novel pruning, feature weighting, and ensemble strategies. The proposed approaches are experimentally validated on well-known real-world datasets, demonstrating their effectiveness and strong practical relevance.

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