1 research outputs found
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 Predictor are automatically valid, however their tightness and
therefore usefulness depends on the nonconformity measure used by each CP. In
effect a nonconformity measure evaluates how strange a given example is
compared to a set of other examples based on some traditional machine learning
algorithm. We define six novel nonconformity measures based on the k-Nearest
Neighbours Regression algorithm and develop the corresponding CPs following
both the original (transductive) and the inductive CP approaches. A comparison
of the predictive regions produced by our measures with those of the typical
regression measure suggests that a major improvement in terms of predictive
region tightness is achieved by the new measures