ALGORITHM FOR SOLVING CLASSIFICATION PROBLEMS USING ARTIFICIAL INTELLIGENCE METHODS

Аннотация

В этой статье рассматриваются алгоритмы решения задач классификации с использованием методов искусственного интеллекта (ИИ). Классификация является фундаментальной задачей в машинном обучении, где цель состоит в том, чтобы назначить точки данных предопределенным категориям или классам на основе их признаков. В статье обсуждаются различные подходы на основе ИИ, такие как алгоритмы контролируемого обучения, деревья решений, машины опорных векторов (SVM), k-ближайших соседей (KNN) и нейронные сети. Также подчеркивается важность обучающих данных, оценки модели и оптимизации при разработке эффективных алгоритмов классификации. В статье подчеркивается Выпуск журнала №-23 Часть–1_Март –2025 201 MODERN EDUCATION AND DEVELOPMENT применение алгоритмов классификации в таких областях, как здравоохранение, финансы и распознавание изображений, а также обсуждаются проблемы, возникающие при их реализации. Ключевые слова. Искусственный интеллект, алгоритмы классификации, контролируемое обучение, деревья решений, машины опорных векторов (SVM), k-ближайших соседей (KNN), нейронные сети, машинное обучение, оценка модели, предварительная обработка данных. Classification problems are a central aspect of machine learning, where the task is to categorize data into distinct classes based on input features. Artificial Intelligence (AI) provides a variety of methods for solving classification problems effectively, including supervised learning algorithms, decision trees, support vector machines, and neural networks. This article explores these AI methods, outlining the steps involved in solving classification tasks, from data preprocessing to model evaluation. The paper also highlights key challenges in classification, such as imbalanced datasets and overfitting, and offers solutions to overcome these challenges. Additionally, the article discusses the real-world applications of classification algorithms across various industries. Classification is one of the most common and important problems in machine learning, where the goal is to assign each input data point to a predefined class. Artificial Intelligence techniques, particularly supervised learning methods, have become instrumental in addressing classification problems by enabling machines to learn from labeled datasets and make predictions on new, unseen data. Classification algorithms are widely used in a variety of domains, including healthcare (diagnostic predictions), finance (fraud detection), and computer vision (image recognition). This article examines the AI methods used for solving classification problems, focusing on their implementation, advantages, and challenges. Classification in Artificial Intelligence. Classification involves the process of assigning labels or categories to data based on input features. In AI, classification is often tackled using supervised learning, where the model is trained on a labeled Выпуск журнала №-23 Часть–1_Март –2025 202 MODERN EDUCATION AND DEVELOPMENT dataset containing both features and corresponding class labels. The model then learns the mapping from input data to output classes. Supervised Learning. Supervised learning is the most common approach for classification tasks. It requires a labeled dataset for training, where each instance is associated with a known class label. The model is trained to learn patterns in the data that correlate with these labels. Common supervised learning algorithms for classification include: Decision Trees. Decision trees are a widely used algorithm in classification tasks. They create a model by splitting the dataset into subsets based on feature values, and each branch of the tree represents a decision rule. The leaf nodes of the tree correspond to the predicted class labels. Support Vector Machines (SVM). Support Vector Machines are powerful classifiers that find the optimal hyperplane that separates the data into different classes. SVMs are particularly effective in high-dimensional spaces and are widely used for tasks like image classification and text classification. K-Nearest Neighbors (KNN). K-Nearest Neighbors is a simple yet effective algorithm that classifies data points based on the majority class of their nearest neighbors in the feature space. KNN does not require training, as it makes decisions based on distance metrics (e.g., Euclidean distance) between the test instance and training data. Neural Networks. Neural networks, particularly deep learning models, have become highly effective in solving complex classification tasks. These models consist of multiple layers of interconnected nodes (neurons), and they are capable of learning complex patterns in large datasets. Neural networks are especially useful in tasks such as image and speech recognition. Steps in Solving Classification Problems. Solving classification problems using AI methods involves several key steps: Data Collection. The first step is gathering a labeled dataset containing features and corresponding class labels.