Thesiss

This dissertation focuses on the classification of one-dimensional and two-dimensional signals. The one-dimensional signal classification problem involves the classification of brain signals for identifying the emotional responses of human subjects under given drug conditions. A…

Dimensionality reduction methods, which embed a set of data observations in a low-dimensional space, are innovative and important tools used in the fields of data analysis, data mining and machine learning. In the first part of this work, we analyze and improve an advanced dimen…

High dimensional data is everywhere in our life and in all sectors of our society: text, image, audio, video, and other. Analyzing such rich data and understanding its behavioral and structural aspects is a fruitful process that will provide us with valuable information and insi…

Comprehending the evolutionary history of the Milky Way can offer great insight into how both our home Galaxy and others form and grow. However, studying the Milky Way from our position embedded within it is difficult, and this challenge is exacerbated by the fact that our obser…

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an opti…