Evaluation metric plays a critical role in achieving the optimal classifier during the
classification training. Thus, a selection of suitable evaluation metric is an important key for …

The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …

A large variety of issues influence the success of data mining on a given problem. Two
primary and important issues are the representation and the quality of the dataset …

Abstract Evolutionary Ensemble Learning (EEL) provides a general approach for scaling
evolutionary learning algorithms to increasingly complex tasks. This is generally achieved …

Data selection, which includes feature and instance selection, is often an important step in
building prediction systems. Genetic algorithms (GA) frequently allow finding better solutions …

In recent years, fuzzy rough set theory has emerged as a suitable tool for performing feature
selection. Fuzzy rough feature selection enables us to analyze the discernibility of the …

Over recent decades, database sizes have grown considerably. Larger sizes present new
challenges, because machine learning algorithms are not prepared to process such large …

Instance selection is becoming increasingly relevant due to the huge amount of data that is
constantly being produced in many fields of research. Although current algorithms are useful …

Instance selection, as an important data pre-processing task, is widely used in supervised
classification. Recently, a series of instance selection algorithms with different techniques …

Stochastic search techniques such as evolutionary algorithms (EA) are known to be better
explorer of search space as compared to conventional techniques including deterministic …