JingTao Yao

Granular Computing: Perspectives and Challenges

Granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. Granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.

Game-Theoretic Rough Sets

This article investigates the Game-theoretic Rough Set (GTRS) model and its capability of analyzing a major decision problem evident in existing probabilistic rough set models. A major challenge in the application of probabilistic rough set models is their inability to formulate a method of decreasing the size of the boundary region through further explorations of the data. To decrease the size of this region, objects must be moved to either the positive or negative regions. Game theory allows a solution to this decision problem by having the regions compete or cooperate with each other in order to find which is best fit to be selected for the move. There are two approaches discussed in this article. First, the region parameters that define the minimum conditional probabilities for region inclusion can either compete or cooperate in order to increase their size. The second approach is formulated by having classification approximation measures compete against each other. We formulate a learning method using the GTRS model that repeatedly analyzes payoff tables created from approximation measures and modified conditional risk strategies to calculate parameter values.

Option price forecasting using neural networks

In this research, forecasting of the option prices of Nikkei 225 index futures is carried out using backpropagation neural networks. Different results in terms of accuracy are achieved by grouping the data differently. The results suggest that for volatile markets a neural network option pricing model outperforms the traditional Black-Scholes model. However, the Black-Scholes model is still good for pricing at-the-money options. In using the neural network model, data partition according to moneyness should be applied. Those who prefer less risk and less returns may use the traditional Black-Scholes model results while those who prefer high risk and high return may choose to use the neural network model results.

Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets

Probabilistic rough set approach defines the positive, negative and boundary regions, each associated with a certain level of uncertainty. A pair of threshold values determines the uncertainty levels in these regions. A critical issue in the community is the determination of optimal values of these thresholds. This problem may be investigated by considering a possible relationship between changes in probabilistic thresholds and their impacts on uncertainty levels of different regions. We investigate the use of game-theoretic rough set (GTRS) model in exploring such a relationship. A threshold configuration mechanism is defined with the GTRS model in order to minimize the overall uncertainty level of rough set based classification. By realizing probabilistic regions as players in a game, a mechanism is introduced that repeatedly tunes the parameters in order to calculate effective threshold parameter values. Experimental results on text categorization suggest that the overall uncertainty of probabilistic regions may be reduced with the threshold configuration mechanism.

A Ten-year Review of Granular Computing

The year 2007 marks the 10th anniversary of the introduction of granular computing research. We have experienced the emergence and growth of granular computing research in the past ten years. It is essential to explore and review the progress made in the field of granular computing. We use two popular databases, ISIs Web of Science and IEEE Digital Library to conduct our research. We study the current status, the trends and the future direction of granular computing and identify prolific authors, impact authors, and the most impact papers in the past decade.

Comparison of term frequency and document frequency based feature selection metrics in text categorization

Text categorization plays an important role in applications where information is filtered, monitored, personalized, categorized, organized or searched. Feature selection remains as an effective and efficient technique in text categorization. Feature selection metrics are commonly based on term frequency or document frequency of a word. We focus on relative importance of these frequencies for feature selection metrics. The document frequency based metrics of discriminative power measure and GINI index were examined with term frequency for this purpose. The metrics were compared and analyzed on Reuters 21,578 dataset. Experimental results revealed that the term frequency based metrics may be useful especially for smaller feature sets. Two characteristics of term frequency based metrics were observed by analyzing the scatter of features among classes and the rate at which information in data was covered. These characteristics may contribute toward their superior performance for smaller feature sets.

Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets

The decision-theoretic rough set (DTRS) model considers costs associated with actions of classifying an equivalence class into a particular region. With DTRS, one may make informative decisions in the form of three-way decisions. Current research mainly focuses on single agent DTRS which is too complex for making a decision when multiple agents are involved. We propose a multi-agent DTRS model and express it in the form of three-way decisions. The new model seeks for synthesized or consensus decisions when there are multiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the conservative, aggressive and majority viewpoints based on the positive, negative and boundary regions made by each agent. These multi-agent decision regions are expressed by figures in the form of three-way decisions.

Information granulation and granular relationships

As an emerging research method to deal with information and knowledge processing, various topics of granular computing have recently received more attention by researchers. The foundations of granular computing involves general principles of many disciplines that are explored for many years such as divide and conquer, interval computing, fuzzy sets, rough sets and so on. Granules, granulations and relationships are some of the key issues in the study of granular computing. This paper aims to understand mainly granular computing theory from the perspective of information granulation and granular relationships.

Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets

The realization of the Web as a common platform, medium, and interface for supporting human activities has attracted many researchers to the study of Web-based support systems (WSS). An important branch of WSS is Web-based decision support systems that provide intelligent support for making effective decisions in different domains. We focus on decision making in Web-based medical decision support systems (WMDSS). Uncertainty is a critical factor that affects decision making and reasoning in the medical field. A three-way decision-making approach is an effective and better choice to lessen the effects of uncertainty. It provides the provision for delaying certain or definite decisions in situations that lack sufficient evidence or accurate information in reaching certain conclusions. Particularly, the option of deferment decisions is added in this approach that provides the flexibility to further examine and investigate the uncertain and doubtful cases. The game-theoretic rough set (GTRS) model is a recent development in rough sets that can be used to determine the three rough set regions in the probabilistic rough sets framework by determining a pair of thresholds. The three regions are used to obtain three-way decision rules in the form of acceptance, rejection, and deferment rules. In this paper, we extend the GTRS model to analyze uncertainty involved in medical decision making. Experimental results with a GTRS-based approach on different health care datasets suggest that the approach may improve the overall quality of decision making in the medical field, as well as other fields. It is hoped that the incorporation of a GTRS component in WMDSS will enrich and enhance its decision-making capabilities.

Criteria for choosing a rough set model

One of the challenges a decision maker faces in using rough sets is to choose a suitable rough set model for data analysis. We investigate how two rough set models, the Pawlak model and the probabilistic model, influence the decision goals of a user. Two approaches use probabilities to define regions in the probabilistic model. These approaches use either user-defined parameters or derive the probability thresholds from the cost associated with making a classification. By determining the implications of the results obtained from these models and approaches, we observe that the availability of information regarding the analysis data is crucial for selecting a suitable rough set approach. We present a list of decision types corresponding to the available information and user needs. These results may help a user match their decision requirements and expectations to the model which fulfills these needs.