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 …

Conventional k-nearest neighbor (KNN) classification approaches have several limitations
when dealing with some problems caused by the special datasets, such as the sparse …

Classification is a method of accurately predicting the target class for an unlabelled sample
by learning from instances described by a set of attributes and a class label. Instance based …

The nearest neighbor (NN) classifier suffers from high time complexity when classifying a
test instance since the need of searching the whole training set. Prototype generation is a …

In recent years, metaheuristic algorithms have emerged as a promising approach to solve
clustering and classification problems. In this paper, gravitational search algorithm (GSA) …

In this chapter, we consider instance selection as an important focusing task in the data
reduction phase of knowledge discovery and data mining. First of all, we define a broader …

The K-nearest neighbor (KNN) rule is one of the most useful supervised classification
methods, and is widely used in many pattern classification applications due to its simplicity …

Differential evolution (DE) is a powerful global optimization algorithm which has been
studied intensively by many researchers in the recent years. A number of variants have been …

Prototype selection aims at reducing the storage of datasets and execution time, and
improving prediction accuracy and operation efficiency by removing noisy or redundant …

Nearest prototype classifier based on differential evolution algorithm, pool of distances and
generalized ordered weighted averaging is introduced. Classifier is based on forming …