Probabilistic Classification from a K-Nearest-Neighbour Classifier
نویسندگان
چکیده
منابع مشابه
An empirical analysis of the probabilistic K-nearest neighbour classifier
The probabilistic nearest neighbour (PNN) method for pattern recognition was introduced to overcome a number of perceived shortcomings of the nearest neighbour (NN) classifiers namely the lack of any probabilistic semantics when making predictions of class membership. In addition the NN method possesses no inherent principled framework for inferring the number of neighbours, K, nor indeed assoc...
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ProbabilisticK-nearest neighbour (PKNN) classification has been introduced to improve the performance of the 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 t...
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Suppose a bank has a database of people’s details and their credit rating. These details would probably be the person’s financial characteristics such as how much they earn, whether they own or rent a house, and so on, and would be used to calculate the person’s credit rating. However, the process for calculating the credit rating from the person’s details is quite expensive, so the bank would ...
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ژورنال
عنوان ژورنال: Computational Research
سال: 2013
ISSN: 2331-995X,2331-9984
DOI: 10.13189/cr.2013.010101