부산대학교 도서관

This study delves into the realm of feature selection and its profound impact on enhancing classification accuracy. Leveraging the renowned Fisher's Iris dataset as a well-established benchmark, our investigation revolves around the application of Chi-squared weights to identify attributes crucial for species classification. The Chi-squared weights are a powerful tool, quantifying the associations between features and species labels. Through this approach, we discern the most pivotal attribute, suggesting its pivotal role in distinguishing iris flower species. By selecting features with elevated Chi-squared weights, we curate a focused feature subset that significantly bolsters classification accuracy.The visualization of Chi-squared weights through intuitive bar graphs emerges as an influential tool for gauging feature importance. This graphical representation aids in identifying attributes that possess strong discriminatory capabilities, thereby empowering informed decision-making during feature selection. The study emphasizes the imperative role of feature selection in the domain of machine learning and classification tasks. By incorporating statistical methodologies like the Chi-squared test, we unravel the essence of attribute significance and streamline the process of crafting potent feature subsets. As datasets evolve in complexity, the identification of pivotal attributes becomes indispensable. The proposed methodology provides a valuable approach to address this challenge.In essence, the study advances feature selection techniques by showcasing the potency of Chi-squared weights and their visual representation in enhancing classification outcomes. Our exploration within the Fisher's Iris dataset manifests the broader potential of informed feature selection strategies in elevating the performance of machine learning models.