International Journal of Information Technology and Decision Making 

About: International Journal of Information Technology and Decision Making is an academic journal published by World Scientific. The journal publishes majorly in the area(s): Computer science & Fuzzy logic. It has an ISSN identifier of 1793-6845. Over the lifetime, 1153 publications have been published receiving 20038 citations. The journal is also known as: Information technology & decision making & Information technology and decision making.

10 challenging problems in data mining research

Abstract: In October 2005, we took an initiative to identify 10 challenging problems in data mining research, by consulting some of the most active researchers in data mining and machine learning for their opinions on what are considered important and worthy topics for future research in data mining. We hope their insights will inspire new research efforts, and give young researchers (including PhD students) a high-level guideline as to where the hot problems are located in data mining. Due to the limited amount of time, we were only able to send out our survey requests to the organizers of the IEEE ICDM and ACM KDD conferences, and we received an overwhelming response. We are very grateful for the contributions provided by these researchers despite their busy schedules. This short article serves to summarize the 10 most challenging problems of the 14 responses we have received from this survey. The order of the listing does not reflect their level of importance.

Evaluation of classification algorithms using mcdm and rank correlation

Abstract: Classification algorithm selection is an important issue in many disciplines. Since it normally involves more than one criterion, the task of algorithm selection can be modeled as multiple criteria decision making (MCDM) problems. Different MCDM methods evaluate classifiers from different aspects and thus they may produce divergent rankings of classifiers. The goal of this paper is to propose an approach to resolve disagreements among MCDM methods based on Spearman's rank correlation coefficient. Five MCDM methods are examined using 17 classification algorithms and 10 performance criteria over 11 public-domain binary classification datasets in the experimental study. The rankings of classifiers are quite different at first. After applying the proposed approach, the differences among MCDM rankings are largely reduced. The experimental results prove that the proposed approach can resolve conflicting MCDM rankings and reach an agreement among different MCDM methods.

A descriptive framework for the field of data mining and knowledge discovery

Abstract: As our abilities to collect and store various types of datasets are continually increasing, the demands for advanced techniques and tools to understand and make use of these large data keep growing. No single existing field is capable of satisfying the needs. Data Mining and Knowledge Discovery (DMKD), which utilizes methods, techniques, and tools from diverse disciplines, emerged in last decade to solve this problem. It brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. Even though data mining has made significant progress during the past fifteen years, most research effort is devoted to developing effective and efficient algorithms that can extract knowledge from data and not enough attention has been paid to the philosophical foundations of data mining. The objective of this research is to provide a conceptual framework that identifies major research areas of data mining and knowledge discovery (DMKD) for students and beginners and describe the longitudinal changes of DMKD research activities. Using the textual documents collected from premier DMKD journals, conference proceedings, syllabi, and dissertations, this study is intended to address the following issues: What are the major subjects of this field? What is the central theme? What are the connections among these subjects? What are the longitudinal changes of DMKD research? To answer these questions, this research uses a combination of grounded theory and document clustering. The result will represent previous and current DMKD research activities in the form of a framework. The resulting framework should allow people to comprehend the entire domain of DMKD research and assist identification of areas in need of more research efforts.

Development of TOPSIS Method to Solve Complicated Decision-Making Problems — An Overview on Developments from 2000 to 2015

Abstract: In recent years several previous scholars made attempts to develop, extend, propose and apply Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for solving problems in decision making issues Indeed, there are questions, how TOPSIS can help for solving these problems? Or does TOPSIS solved decision making problems in the real world? Therefore, this study shows the recent developments of TOPSIS approach which are presented by previous scholars To achieve this objective, there are 105 reviewed papers which developed, extended, proposed and presented TOPSIS approach for solving DM problems The results of the study indicated that 49 scholars have extended or developed TOPSIS technique and 56 scholars have proposed or presented new modifications for problems solution related to TOPSIS technique from 2000 to 2015 In addition, results of this study indicated that, previous studies have modifications related to this technique in 2011 more than other years

A Hybrid Method for Pythagorean Fuzzy Multiple-Criteria Decision Making

Abstract: As a generalization of intuitionistic fuzzy set, the Pythagorean fuzzy set is interesting and very useful in modeling uncertain information in real-world decision-making problems. In this paper, we develop a new method for Pythagorean fuzzy multiple-criteria decision-making (MCDM) problems with aggregation operators and distance measures. First, we present the Pythagorean fuzzy ordered weighted averaging weighted average distance (PFOWAWAD) operator. The main advantage of the PFOWAWAD operator is that it uses distance measures in a unified framework between the ordered weighted averaging (OWA) operator and weighted average (WA) that considers the degree of importance of each concept in the aggregation. Some of its main properties and special cases are studied. Then, based on the proposed operator, a hybrid TOPSIS method, called PFOWAWAD-TOPSIS is introduced for Pythagorean fuzzy MCDM problem. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed method.