An Application of Rough Set Analysis toa Psycho-Physiological Study - Assessing the RelationBetween Psychological Scale and Immunological Biomarker

2009 ◽  
Vol 13 (4) ◽  
pp. 352-359
Author(s):  
Shusaku Nomura ◽  
◽  
Yasuo Kudo ◽  
Keyword(s):  
Rough Set ◽  
Linear Correlation ◽  
Mental Stress ◽  
Rough Set Theory ◽  
Physiological State ◽  
Mental States ◽  
Psychological State ◽  
Analysis Method ◽  
Psychological Scale ◽  
The Relationship

This study aims at an application of rough set theory to illustrate the relationship between human psychological and physiological states. Recent behavioral medicine studies have revealed that various human secretory substances change according to mental states. These substances, the hormones and immune substances, show temporal increase against mental stress. Thus, it is frequently introduced as biomarkers of mental stress. The relationship between these biomarkers and human chronic stresses or daily mental states was also suggested in the previous studies. However the results of these studies were sometimes inconsistent with each other. Some technical reasons were indicated for this discrepancy. Among that, we focused on the analysis technique investigating the relationship between human psychological state, i.e., scores of a psychological scale, and physiological state, i.e., level of the secretory biomarkers. In this paper, we introduced Rough Set analysis method instead of using a conventional linear correlation analysis method. In the experiment, the salivary secretory immunoglobulin A (IgA), which is a major stress biomarker, of 20 male students was assessed before and after a short-term stressful mental workload. Also, 65 items of psychological mood scale was assessed as a psychological index. The result showed that some items strongly related with the change in the IgA, while no significant linear correlation was obtained among them.

The Application of Data Mining in the Civil Aviation Accident Analysis

2012 ◽  
Vol 241-244 ◽  
pp. 3000-3004
Author(s):  
Dai Wu Zhu ◽  
Yin Ni
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Passive State ◽  
Civil Aviation ◽  
Accident Analysis ◽  
Analysis Method ◽  
Accident Prediction

At present, our analysis of the aviation accident mainly limited to the methods of mathematical statistics, the analysis method means of a single, and in a passive state, so the accident prediction is poor. This paper, basis on the rough set theory in data mining and preferential information ,we improve the rough set attribute reduction algorithm, and applied to civil aviation accident analysis to indentify the potential law of accident.

scholarly journals Evolving Lattices for Analyzing Behavioral Dynamics of Characters in Literary Text

2011 ◽  
Vol 9 (2) ◽  
pp. 502-509
Author(s):  
Eugene S Kitamura ◽  
Yukio-Pegio Gunji
Keyword(s):  
Rough Set ◽  
Text Analysis ◽  
Rough Set Theory ◽  
Window Size ◽  
Literary Text ◽  
Equivalence Relations ◽  
Behavior Dynamics ◽  
Behavioral Dynamics ◽  
The Relationship ◽  
Double Approximation

This paper is about an application of rough set derived lattices in order to analyze the dynamics of literary text. Due to the double approximation nature of rough set theory, a pseudo-closure obtained from two different equivalence relations allows us to form arbitrary lattices. Moreover, such double approximations with different equivalence relations permit us to obtain lattice fixed points based on two interpretations. The two interpretations used for literary text analysis are subjects and their attributes. The attributes chosen for this application are verbs. The progression of a story is defined by the sequence of verbs (or event occurrences). By fixing a window size and sliding the window down the story steps, we obtain a lattice representing the relationship between subjects and their attributes within that window frame. The resulting lattice provides information such as complementarity (lattice complement existence rate) and distributivity (lattice complement possession rate). These measurements depend on the overlap and the lack of overlap among the attributes of characters. As the story develops and new character and attributes are provided as the source of lattices, one can observe its evolution. In fact, a dramatic change in the behavior dynamics in a scene is reflected in the particular shifts in the character-attribute relationship. This method lets us quantify the developments of character behavioral dynamics in a story.

scholarly journals Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Aboul ella Hassanien ◽  
Mohamed E. Abdelhafez ◽  
Hala S. Own
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Confusion Matrix ◽  
Decision Rules ◽  
Minimal Subset ◽  
Discretization Algorithm ◽  
Diabetic Children ◽  
The Relationship ◽  
Accuracy Rates

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

scholarly journals A COMPARISON BETWEEN MRA AND ROUGH SET THEORY FOR MASS APPRAISAL. A CASE IN BARI

2004 ◽  
Vol 8 (4) ◽  
pp. 205-217 ◽  
Author(s):  
Maurizio D'amato
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Property Market ◽  
Specific Group ◽  
Market Data ◽  
Property Valuation ◽  
A Value ◽  
Valuation Methodology ◽  
The Relationship

Rough Set Theory is a property valuation methodology recently applied to property market data (d'Amato, 2002). This methodology may be applied in property market where few market data are available or where econometric analysis may be difficult or unreliable. This methodology was introduced by a polish mathematician (Pawlak, 1982). The model permit to estimate a property without defining an econometric model, although do not give any estimation of marginal or hedonic prices. I : ,he first version of RST was necessary to organize the data in classes before the valuation .The relationship between these classes defined if‐then rules. If a property belongs to a specific group then it will belong to a class of value. The relationship between the property and the class of value is dichotomous. In this paper will be offered a second version that improve the RST with a “value tolerance relation” in order to make more flexible the rule. In this case the results will come out from an explicit and specific relationship. The methodology has been tested on 69 transactions in the zone of Carrassi-Poggiofranco in the residential property market of Bari.

COMBINATION ENTROPY AND COMBINATION GRANULATION IN ROUGH SET THEORY

2008 ◽  
Vol 16 (02) ◽  
pp. 179-193 ◽  
Author(s):  
YUHUA QIAN ◽  
JIYE LIANG
Keyword(s):  
Mutual Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Information Gain ◽  
Heuristic Function ◽  
Nature Of Information ◽  
Knowledge Content ◽  
Incomplete Information Systems ◽  
The Relationship

Based on the intuitionistic knowledge content nature of information gain, the concepts of combination entropy and combination granulation are introduced in rough set theory. The conditional combination entropy and the mutual information are defined and their several useful properties are derived. Furthermore, the relationship between the combination entropy and the combination granulation is established, which can be expressed as CE(R) + CG(R) = 1. All properties of the above concepts are all special instances of those of the concepts in incomplete information systems. These results have a wide variety of applications, such as measuring knowledge content, measuring the significance of an attribute, constructing decision trees and building a heuristic function in a heuristic reduct algorithm in rough set theory.

Comparative Analysis of Hybrid Soft Set Methods

2014 ◽  
pp. 360-383
Author(s):  
Debadutta Mohanty
Keyword(s):  
Information System ◽  
Comparative Analysis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Soft Set ◽  
Mathematical Tool ◽  
Soft Rough Set ◽  
Definition Of ◽  
The Relationship

The whole mathematical scenario has changed with the advent of the Rough Set Theory, a powerful tool to deal with uncertainty and incompleteness of knowledge in information system. With the advancement of research, the Soft Set Theory has emerged as an advanced mathematical tool to deal with data associated with uncertainty. The present chapter endeavors to forge a connection between soft set and rough set and maps a new model rough soft set to address the challenges of vagueness and impreciseness. Although the research contribution of M. Irfan Ali, Dan Meng, et al. and Feng Feng et al. had given distinct definition of rough soft set and soft rough set, the analysis explaining the genesis of these sets is not appropriate. This chapter is a new attempt to construct the relationship between a rough set, soft set, and fuzzy set to form a hybrid soft set giving a concrete comprehensive definition of rough soft set in border perspective.

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