BUILDING AN ALGORITHM FOR SOLVING CLASSIFICATION PROBLEMS USING ARTIFICIAL INTELLIGENCE METHODS

Аннотация

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. Keywords. Artificial Intelligence, Classification Problems, Machine Learning, Decision Trees, Support Vector Machines, Neural Networks, Feature Selection, Hyperparameter Optimization, Model Evaluation, Data Preprocessing. Classification is a key area of research and application within the field of machine learning, where the goal is to categorize data into predefined classes based on input features. From medical diagnoses to fraud detection, classification problems are Выпуск журнала №-26 Часть–6_ Май –2025 113 MODERN EDUCATION AND DEVELOPMENT ubiquitous in real-world applications. Traditionally, classification tasks have been approached using a variety of methods, including statistical techniques and rule-based systems. However, recent advancements in artificial intelligence (AI) and machine learning have provided more powerful and flexible approaches to classification. These AI methods, such as decision trees, support vector machines (SVM), and neural networks, have demonstrated impressive performance across diverse domains. This paper proposes a unified algorithm that combines these AI techniques to address classification problems effectively, with an emphasis on maximizing accuracy and generalization. Over the past few decades, numerous AI methods have been developed to solve classification problems. Decision trees, such as the ID3 and C4.5 algorithms, have long been used for their simplicity and interpretability. Support vector machines (SVM) are known for their ability to handle high-dimensional data and achieve high accuracy in binary classification tasks. More recently, neural networks, particularly deep learning models, have gained significant attention due to their ability to automatically learn complex patterns from large datasets. Ensemble methods, such as random forests and boosting algorithms, combine multiple models to improve performance. While many of these algorithms have proven successful individually, few studies have explored how different machine learning techniques can be integrated into a single algorithm to solve classification problems more effectively. This paper seeks to bridge that gap by proposing an integrated approach that combines the strengths of multiple AI methods. The proposed algorithm for solving classification problems consists of several key components: data preprocessing, feature selection, model selection, training, and evaluation. The algorithm begins with data preprocessing, which includes normalization, handling missing values, and encoding categorical variables. Next, feature selection is performed to identify the most relevant attributes for classification, reducing dimensionality and improving model efficiency. Various machine learning models—decision trees, SVM, and neural networks—are trained on the preprocessed data. The algorithm automatically selects the best model based on the dataset and task requirements, using techniques like cross-validation and grid search for Выпуск журнала №-26 Часть–6_ Май –2025 114 MODERN EDUCATION AND DEVELOPMENT hyperparameter optimization. The algorithm's performance is evaluated using standard classification metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is a critical step in any machine learning task, as it ensures the quality and consistency of the input data. In this step, the algorithm performs operations such as normalization (scaling features), missing value imputation, and encoding categorical variables into numerical formats. This ensures that the machine learning models can process the data effectively. Feature selection aims to identify and retain the most informative features while removing irrelevant or redundant ones. Techniques like mutual information, recursi