Performance analysis of k-Nearest Neighbors classification on Reuters news article datasets

Abstract

The k-Nearest Neighbors (k-NN) algorithm is a fundamental and widely-used classification technique that has found applications in various domains, including text classification. In this paper, we present a comprehensive analysis of the k-NN classification algorithm applied to the Reuters news article dataset. Our study includes the data, implementation k-NN classification with different parameters, performance evaluation, and statistical analysis to draw meaningful conclusions. In a comprehensive analysis of the k-NN classification algorithm used for the Reuters news article data-set. A variety of metrics is used to evaluate the performance of the k-NN algorithm, such as accuracy, precision, recall, and F1 scores. These metrics provide a comprehensive view of how well the algorithm classifies news articles. Our statistical analysis reveals significant performance differences between various k-NN configurations. This can help researchers and practitioners make informed decisions when choosing the best parameters for their specific text classification tasks. In conclusion, our study provides valuable insights into the application of k-NN classification algorithms to textual data, highlighting the importance of parameter tuning and rigorous evaluation. These findings can guide practitioners to effectively use k-NN for text classification tasks and inspire further research in the field.