AI-Driven Pattern Recognition Using Optimized Tree Structures
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P102Keywords:
Optimized Decision Trees, Ensemble Learning, AI-Driven Pattern Recognition, Tree Pruning, Reinforcement Learning, Evolutionary Algorithms, Computational Efficiency, Overfitting Reduction, Scalability, Explainable AIAbstract
Pattern recognition is a critical aspect of Artificial Intelligence (AI) and machine learning (ML), enabling systems to identify and classify patterns in data. Traditional pattern recognition techniques, such as decision trees and random forests, have been widely used but often suffer from issues like overfitting, suboptimal splits, and high computational complexity. This paper introduces an optimized tree structure approach that leverages AI-driven techniques to enhance pattern recognition. The proposed method combines advanced tree optimization algorithms with ensemble learning to improve accuracy, efficiency, and robustness. We present a comprehensive evaluation of the proposed method using various datasets and compare it with existing state-of-theart techniques. The results demonstrate significant improvements in performance, making the optimized tree structure a promising approach for AI-driven pattern recognition
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