It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours are found for more than one training class (model-1). We also propose model-2 of nearest-neighbour ...

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 ...

The Approximating and Eliminating Search Algorithm (AESA) and related AESA-based techniques are among the fastest methods for (k-)Nearest Neighbour(s) searching in general metric spaces. These techniques can be optimized for the (easier) (k-)Nearest Neighbour(s) classification problem. In particular, an optimized version of the AESA is proposed here which is shown to be significantly faster tha...

We derive a new asymptotic expansion for the global excess risk of a local k-nearest neighbour classifier, where the choice of k may depend upon the test point. This expansion elucidates conditions under which the dominant contribution to the excess risk comes from the locus of points at which each class label is equally likely to occur, but we also show that if these conditions are not satisfi...

PT. Dasatama Cemerlang Motor is a company engaged in the automotive sector. With increasingly fierce competition among industry, companies are required to be able handle inter-industry competition. Sales system uses cash or credit system. For every motorcycle sale, admin inputs sales data using Ms.Excel. Even though Ms.Excel has many features and functions that used process numbers, it cannot p...

Text classification is the process of grouping documents based on similarity in categories. Some obstacles doing text are many words appeared text, and some come up with infrequent frequency (sparse words). The way to solve this problem conduct feature selection process. There several filter-based methods; Chi-Square, Information Gain, Genetic Algorithm, Particle Swarm Optimization (PSO). Aghda...