Overview

The framework described in this research paper focuses on data preprocessing for classification tasks in data mining, utilizing intelligent software agents. The aim is to enhance the efficiency and effectiveness of data preparation stages by automating and optimizing various preprocessing tasks. The intelligent agents are designed to analyze and transform the raw data, addressing challenges such as missing values, noise, feature selection, and data integration. By leveraging intelligent techniques, such as machine learning and artificial intelligence algorithms, the framework intelligently handles data preprocessing tasks to improve the quality of input data for classification models. The agents autonomously perform data cleansing, normalization, dimensionality reduction, and other preprocessing operations. This enables researchers and practitioners to streamline the data preparation process and obtain more accurate and reliable results in their classification tasks. The proposed framework has implications for various domains that involve data mining and classification, including business analytics, healthcare, finance, and scientific research. By automating and optimizing data preprocessing, it reduces the burden on data scientists and analysts, enabling them to focus on higher-level tasks and decision-making. The intelligent agent-based approach contributes to advancing the field of data mining by improving the efficiency and effectiveness of classification tasks through intelligent data preprocessing.

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