BUILDING AN ALGORITHM FOR SOLVING CLASSIFICATION PROBLEMS USING ARTIFICIAL INTELLIGENCE METHODS

Abstract

Classification is one of the most fundamental tasks in machine learning, playing a critical role in a wide variety of applications, such as image recognition, medical diagnostics, and natural language processing. This paper introduces an algorithm designed to address classification problems using artificial intelligence (AI) methods, specifically focusing on machine learning techniques such as decision trees, support vector machines (SVM), and neural networks. The proposed algorithm is capable of handling both binary and multi-class classification problems, providing a robust solution that can be applied across different domains. The algorithm incorporates preprocessing techniques, feature selection, and hyperparameter optimization to improve performance and generalizability. Experimental results using publicly available datasets demonstrate the effectiveness of the algorithm in terms of accuracy, precision, recall, and F1-score. The paper also explores the trade-offs involved in selecting different machine learning models and the challenges associated with imbalanced data. In conclusion, the proposed AI-based algorithm offers a versatile and efficient tool for solving classification problems across a range of applications.