Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
This work addresses classification accuracy issues in geo-spatial data analysis, offering an incremental improvement over existing adaptive kNN methods.
The paper tackles the problem of suboptimal global k in kNN classification for datasets with irregular density by proposing an adaptive kNN classifier that dynamically selects k per instance to maximize expected accuracy, demonstrating improved performance over common and prior adaptive kNN algorithms on geo-spatial data tasks with thousands of items.
The k-Nearest Neighbor (kNN) classification approach is conceptually simple - yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e.g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We define the expected accuracy as the accuracy of a set of structurally similar observations. An arbitrary similarity function can be used to find these observations. We introduce and evaluate different similarity functions. For the evaluation, we use five different classification tasks based on geo-spatial data. Each classification task consists of (tens of) thousands of items. We demonstrate, that the presented expected accuracy measures can be a good estimator for kNN performance, and the proposed adaptive kNN classifier outperforms common kNN and previously introduced adaptive kNN algorithms. Also, we show that the range of considered k can be significantly reduced to speed up the algorithm without negative influence on classification accuracy.