Nowadays, the use of metaheuristic algorithms has dramatically increased in order to
achieve the optimal solution in solving continuous optimization problems. In this paper, a …

Many strategies have been exploited for the task of feature selection, in an effort to identify
more compact and better quality feature subsets. A number of evaluation metrics have been …

This paper proposes three feature selection algorithms with feature weight scheme and
dynamic dimension reduction for the text document clustering problem. Text document …

High dimensionality problem is an important concern for short text classification due to its
effect on computational cost and accuracy of classifiers. Also, short text data, besides being …

Misclassification costs of minority class data in real-world applications can be very high. This
is a challenging problem especially when the data is also high in dimensionality because of …

Feature selection (FS) is mainly used as a pre-processing tool to reduce dimensionality by
eliminating irrelevant or redundant features to be used for a machine learning or data mining …

Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance …

A rule base covering the entire input domain is required for the conventional Mamdani
inference and Takagi–Sugeno–Kang (TSK) inference. Fuzzy interpolation enhances …

The increasing amount of text documents in digital forms affect the text analysis techniques.
Text clustering (TC) is one of the important techniques used for showing a massive amount …

From a neo-institutional perspective, this chapter investigates the function and interaction of
globalization and decentralization in China's higher education administrative reform. Both …