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.

Subhashree P

Department of Marketing

VIT Business School

Vellore Campus

Acharjya B

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

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

International Journal of Business Analytics

In Indian economy, agriculture is the prime vocation that avails in the overall development of the country. Tamil Nadu occupies approximately 7% of the nation's population, with 3% of water resources and 4% of land resources at the country level. The crop suitability prediction is of prime importance to enhance the nutritional security to the developing country. Based on several crops grown in a particular place, and the availability of natural resources, one can identify the suitability of crops that can be grown in a particular place. To this end, many mathematical tools were developed, but they failed to include processing of uncertainties present in the accumulated data. Therefore, in this paper an effort has been made to process the uncertainties by hybridizing rough set on fuzzy approximation space and neural network. The rough set on fuzzy approximation space identifies the almost indiscernibility among the natural resources and helps in minimizing the computational procedure on employing data reduction techniques, whereas neural network helps in prediction process. The proposed model is analysed on agriculture data of Vellore District of Tamil Nadu, India, and achieved 93% of classification accuracy in validation. The model is compared with an earlier model and achieved 8% more accuracy while predicting unseen associations. © 2017, The Natural Computing Applications Forum.

Neural Computing and Applications

Crop suitability prediction in Vellore District using rough set on fuzzy approximation space and neural network

This article describes how agriculture is the main occupation of India, and how the economy depends on agricultural production. Most of the land in India is dedicated to agriculture and people depend on the production of agricultural products. Therefore, forecasting the accuracy of future events based on extracted patterns plays a vital role in improving agricultural productivity. By considering the availability of micronutrients and macronutrients of the soil and water in a particular place, the growth of a plant is determined. This helps people to determine the crops to be cultivated at a certain place. In this article, the forecasting is carried out using rough sets and genetic algorithms. Rough sets are used to produce the decision rules whereas genetic algorithms are used to refine the rules and improve classification accuracy. Accuracy of the classification rules is analyzed using different selection methods and crossover operators. Results show that genetic algorithms with a roulette wheel selection and single point crossover provides better performance when compared with other existing techniques.

International Journal of Fuzzy System Applications

A Rule Based Classification for Vegetable Production Using Rough Set and Genetic Algorithm

Marketing management employs various tools and techniques, including market research, to perform accurate marketing analysis. Information and communication technology provided a new dimension in marketing research to maximise the revenues and profits of the firm by identifying the chief attributes affecting decisions. In this paper, we present a hybrid approach for attribute selection in marketing based on rough computing and formal concept analysis. Our approach is aimed at handling an information system that contains numerical attribute values that are “almost similar” instead of “exact similar”. To handle such an information system we use two processes—pre-process and post-process. In pre-process, we use rough set on intuitionistic fuzzy approximation space with ordering rules to find knowledge and associations, whereas in post-process we use formal concept analysis to identify the chief attributes affecting decisions. © 2017

Fulltext

IIMB Management Review

A framework for attribute selection in marketing using rough computing and formal concept analysis

Convergence of information and communication on technology changed the life of a common man over the last few years. At the same time it increases threats to a common man. In order to overcome these situations lots of research is carried out in the direction network and internet security. In addition identification of various attacks is of great challenge before taking any security measures. Identification of phishing attacks such as fake e-mails or websites is one such critical attack in internetworking. It is an illegal trick game that collects personal information from a legitimate user. The fake e-mails or website looking like a true one and even in the web pages where users are asked to send their personal information may look like a legitimate. Identification of such websites, e-mails is one of great challenge today. To this end, in this paper, we propose a model for identification of phishing attacks and chief attributes that make an object phishing object using rough set and formal concept analysis. The concept is explained with an illustration and followed by a case study. The results obtained show the viability of the proposed research. © 2017 Inderscience Enterprises Ltd.

International Journal of Communication Networks and Distributed Systems

A framework for phishing attack identification using rough set and formal concept analysis

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

Journal	International Journal of Business Analytics
Publisher	IGI Global
ISSN	2334-4547
Open Access	0