Enhancing Classification Accuracy With K-Nearest Neighbors: Optimizing Distance Metrics and Handling Unlabeled Data

The K-Nearest Neighbors (K-NN) is a supervised machine learning algorithm, specifically within the realm of classification and regression tasks. It is a simple yet powerful method used for making predictions based on similarities between data points. The fundamental idea behind K-NN is to classify or predict a new data point's label or classify by looking at the labels of its nearest neighbors in the training dataset. This research introduces an adaptive K-NN classification approach that leverages local data characteristics to dynamically adjust neighborhood size and distance metrics. This adaptive K-NN classification approach is thoroughly examined through comparisons with \(k=3\), \(k=5\) and \(k=7\). By adjusting neighborhood size and distance metrics, the study yields nuanced performance insights. Calculated outlier-to-class distances offer valuable adaptability indications. The experimental results showcase its potential to enhance classification accuracy and adaptability in diverse data scenarios. This method contributes to the advancement of K-NN based classification techniques and provides a promising direction for improving the efficiency of data classification tasks in real-world applications.