As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans in extracting knowledge from this rapidly growing digital data. Global Trends in Intelligent Computing Research and Development brings together recent advances and in depth knowledge in the fields of knowledge representation and computational intelligence. Highlighting the theoretical advances and their applications to real life problems, this book is an essential tool for researchers, lecturers, professors, students, and developers who have seek insight into knowledge representation and real life applications.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

Debi Prasanna Acharjya

Department of Computational Intelligence

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Global Trends in Intelligent Computing Research and Development

Advances in Computational Intelligence and Robotics

Behavioural finance has gained research interest among researchers because of investor behavior and market anomalies. Investor behaviour varies with demographics and geographic characteristics. Further, investor behavior towards a gold exchange trade fund is gaining research interest due to various factors. Not much research has been carried out in this direction, with the exception of some comparisons. Therefore, the performance of a gold exchange traded fund needs to be assessed from the investor behavior perspective. Additionally, the investors behavior contains uncertainties. Thus, there is a need for intelligent techniques for identifying the investors behavior despite the presence of uncertain behavioral characteristics. Therefore, to study uncertain behavior characteristic in gold exchange traded fund, in this article the authors employ a fuzzy rough set. They employ fuzzy rough quick reduct algorithm to find the superfluous attributes. Further decision rules are generated to identify the chief feature of investors' behavior towards gold exchange traded fund.

International Journal of Business Analytics

A Fuzzy Rough Feature Selection Framework for Investors Behavior Towards Gold Exchange-Traded Fund

Discovering important concepts in formal concept analysis (FCA) is an important issue due to huge number of concepts arising out of complicated contexts. To address this issue, this paper proposes a method for concept compression in FCA, involving many-valued decision context, based on information entropy. The precedence order of attributes is obtained by using entropy theory developed by Shannon. The set of concepts is compressed using the precedence order thus determined. An algorithm namely Entropy based concept compression (ECC) is developed for this purpose. Further, similarity measures between the actual and compressed concepts are examined using the deviance analysis and percentage error calculation on the deviance of input weights of concepts. From the experiments, it is found that the compressed concepts inherit association rules to the maximum extent. © 2017 Taylor &amp; Francis.

Journal	Advances in Computational Intelligence and Robotics
Publisher	IGI Global
ISSN	2327-0411
Open Access	0