In the era of big data, analyzing and extracting knowledge from large-scale data sets is a
very interesting and challenging task. The application of standard data mining tools in such …

The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve
classification and pattern recognition tasks. Despite its high classification accuracy, this rule …

Nowadays, many disciplines have to deal with big datasets that additionally involve a high
number of features. Feature selection methods aim at eliminating noisy, redundant, or …

Pattern selection methods have been traditionally developed with a dependency on a
specific classifier. In contrast, this paper presents a method that selects critical patterns …

The K-nearest neighbor (KNN) rule is one of the most widely used pattern classification
algorithms. For large data sets, the computational demands for classifying patterns using …

Nearest neighbor classification is one of the most used and well known methods in data
mining. Its simplest version has several drawbacks, such as low efficiency, high storage …

Abstract k-Nearest neighbor (KNN) rule is a very simple and powerful classification
algorithm. In this article, we propose a new KNN-based classifier, called the local mean …

In the recent times, several disciplines have to tackle with huge datasets, which are involved
with a huge number of additional features. Feature Selection (FS) techniques target at …

In online advertising, the user-clicks dataset based fraudulent publishers' classification
models exhibit poor performance due to high skewness in class distribution of the …

Prototype Generation (PG) methods are typically considered for improving the efficiency of
the k-Nearest Neighbour (k NN) classifier when tackling high-size corpora. Such …