Preliminary Bulbous Bow Design Tool Applying K Nearest Neighbours Classification and Regression Model

Nearest Neighbours without k: A Classification Formalism Based on Probability

Regression Conformal Prediction with Nearest Neighbours

In this paper we apply Conformal Prediction (CP) to the k -Nearest Neighbours Regression (k -NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Pr...

K nearest neighbours with mutual information for simultaneous classification and missing data imputation

Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours ðKNNÞ algorithm. In this article, we propose a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI). This method provides a missing data estimation aimed at solving the c...

Instance and Feature Weighted k-Nearest-Neighbours Algorithm

We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process –assigning different importances to instances– as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in ...

Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification

Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, k. The contribution of this paper is to incorporate the ...