knn2nb

Knn2nb is a hybrid machine learning technique that integrates k-nearest neighbors (k-NN) with a Naive Bayes (NB) classifier. The aim is to combine the local, instance-level information captured by k-NN with the probabilistic, scalable framework of NB. In knn2nb approaches, neighborhood information is used to inform or modify the class-conditional probability estimates of NB, or to create neighborhood-based features that are fed into a Naive Bayes model.

There are several ways knn2nb can be realized. One common interpretation is to use the statistics of the k nearest labeled samples to influence the likelihood estimates P(x|C) in NB, often through local smoothing or by deriving a neighborhood-informed prior P(C). Another variant constructs features from neighborhood counts or distances and trains a standard Naive Bayes classifier on this augmented feature set. Some implementations train a dedicated local model for each neighborhood, while others build a single NB model whose parameters are adjusted by neighborhood information.

Training and prediction with knn2nb typically involve first building a k-NN graph from the training data. For a new instance, the k nearest neighbors are retrieved, and their class distribution or distance-based statistics are used to adjust NB probabilities. The resulting posterior probabilities are then used to make the final class decision, similar to conventional NB, but with neighborhood context integrated into the probability estimates.

Knn2nb can be advantageous on datasets where NB’s feature independence assumption is violated but where local structure is informative. Trade-offs include added computational cost for neighbor retrieval, sensitivity to the choice of k and distance metric, and potential overfitting if neighborhoods are not representative of the underlying class distribution.