Heart Disease Prediction System Using K- Nearest Neighbour Classification Technique

Fuzzy-rough nearest neighbour classification and prediction

In this paper, we propose a nearest neighbour algorithm that uses the lower and upper approximations from fuzzy rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other nearest neighbour approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction ...

Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour

Arabic text classification using k-nearest neighbour algorithm

Many algorithms have been implemented to the problem of Automatic Text Categorization (ATC). Most of the work in this area has been carried out on English texts, with only a few researchers addressing Arabic texts. We have investigated the use of the K-Nearest Neighbour (K-NN) classifier, with an Inew, cosine, jaccard and dice similarities, in order to enhance Arabic ATC. We represent the datas...

k-Nearest Neighbour Classifiers

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...

Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction

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 ...