Multi-Feature Classification Enhancement

This research proposes an improvement for distance-based classification algorithms, specifically focusing on the K-nearest neighbors (KNN) algorithm. The proposed method involves dividing the chosen da taset into multiple clusters and subsequently running KNN on each cluster separately. Each cluster represents a separate subset of data. By partitioning the dataset into clusters, the main goal is to reduce the complexity of the classification within each subset. The analysis of the new model presents improvements in the predictions in the sub-datasets compared to the transitional distance analysis prediction. Our study's results highlight the proposed method's potential to enhance distance-based classification algorithms. The model can be used for a variety of market domains and achieve better performance in the classification of critical tasks. It is recommended that future research continues to explore and refine this method to advance distance-b ased classification algorithms and facilitate more accurate and reliable predictions in various fields and use cases.