Feature Selection and Model Optimization for Accurate Classification of Complex Data

Abstract

The present study aimed to investigate the role of feature selection and model optimization in improving the classification performance of machine learning algorithms for complex data. A quantitative, experimental, and comparative research design was adopted using a structured dataset collected from a relevant data source in the selected study context. The dataset comprised the observations and predictor variables required for classification, while data collection involved obtaining the dataset, identifying the target variable, and preparing the data for analysis. Data preprocessing included checking missing values, removing duplicate observations where necessary, identifying inconsistencies, and dividing the dataset into training and testing subsets. Feature selection techniques were applied to identify informative variables and reduce data dimensionality, while hyperparameter optimization was performed to identify suitable configurations for Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting algorithms. Python-based data analysis was conducted using relevant data processing and machine learning libraries. Model performance was evaluated using accuracy, precision, recall, and F1-score, and the results were compared between baseline models and models developed using feature selection and optimization. The study provides a systematic framework for examining the combined contribution of feature selection and model optimization to classification performance, computational efficiency, and model generalizability. Its significance lies in supporting the development of more efficient and reliable classification systems for complex datasets and providing methodological guidance for researchers applying machine learning in different fields. The findings are expected to contribute to improved understanding of how data dimensionality reduction and appropriate model configuration can support predictive performance, subject to empirical validation using the actual dataset.