expanded-knn

Expanded KNN (EKNN) is a variant of the K-nearest neighbor (KNN) algorithm that takes into account the local density of the data points when making predictions. In traditional KNN, the k-nearest neighbors of a new data point are used to determine its class label. However, this approach can be sensitive to outliers or noisy data points, as they can skew the results.

In EKNN, a local density estimate is used to weight the contributions of the k-nearest neighbors. The density estimate is based on the distance between the new data point and its k-nearest neighbors, as well as the distance between those neighbors themselves. This allows EKNN to be more robust to outliers and noise, as the contributions of these points will be reduced if they are far from the rest of the data.

EKNN has been shown to outperform traditional KNN in a variety of datasets, particularly those with complex or high-dimensional data. However, it can be computationally expensive, as it requires calculating the local density estimate for each new data point.