scholarly journals Novel Parameterized Utility Function on Dual Hesitant Fuzzy Rough Sets and Its Application in Pattern Recognition

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Zhongjun Wu ◽  
Fangwei Zhang ◽  
Jing Sun ◽  
Wenjing Wang ◽  
Xufeng Tang
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Utility Function ◽  
Traffic Engineering ◽  
Rough Sets ◽  
Utility Functions ◽  
Fuzzy Rough Sets ◽  
Static Data ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

Based on comparative studies on correlation coefficient theory and utility theory, a series of rules that utility functions on dual hesitant fuzzy rough sets (DHFRSs) should satisfy, and a kind of novel utility function on DHFRSs are proposed. The characteristic of the introduced utility function is a parameter, which is determined by decision-makers according to their experiences. By using the proposed utility function on DHFRSs, a novel dual hesitant fuzzy rough pattern recognition method is also proposed. Furthermore, this study also points out that the classical dual tool is suitable to cope with dynamic data in exploratory data analysis situations, while the newly proposed one is suitable to cope with static data in confirmatory data analysis situations. Finally, a medical diagnosis and a traffic engineering example are introduced to reveal the effectiveness of the newly proposed utility functions on DHFRSs.

The Often-Overlooked Power of Summary Statistics in Exploratory Data Analysis: Comparison of Pattern Recognition Entropy (PRE) to Other Summary Statistics and Introduction of Divided Spectrum-PRE (DS-PRE)

2021 ◽  
Vol 61 (9) ◽  
pp. 4173-4189
Author(s):  
Tahereh G. Avval ◽  
Behnam Moeini ◽  
Victoria Carver ◽  
Neal Fairley ◽  
Emily F. Smith ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Summary Statistics ◽  
Exploratory Data

Hyperbolic Space for Interactive Visualization

2011 ◽  
pp. 575-581
Author(s):  
Jörg Andreas Walter
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Hyperbolic Space ◽  
Exploratory Data Analysis ◽  
Strong Support ◽  
Interactive Visualization ◽  
User Interaction ◽  
Efficient Integration ◽  
Exploratory Data ◽  
The Given

For many tasks of exploratory data analysis, visualization plays an important role. It is a key for efficient integration of human expertise — not only to include his background knowledge, intuition and creativity, but also his powerful pattern recognition and processing capabilities. The design goals for an optimal user interaction strongly depend on the given visualization task, but they certainly include an easy and intuitive navigation with strong support for the user’s orientation.

Descriptive Data Analysis: A Concept between Confirmatory and Exploratory Data Analysis

1987 ◽  
Vol 26 (02) ◽  
pp. 77-88 ◽  
Author(s):  
K. Abt
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Analytical Models ◽  
Controlled Study ◽  
Complex Data ◽  
Descriptive Data ◽  
Relevant Effect ◽  
Significance Levels ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

SummaryConfirmatory Data Analysis (CDA) in randomized comparative (“controlled”) studies with many variables and/or time points of interest finds its limitations in the multiplicity of desired inferential statements which leads to unfeasibly small adjusted significance levels (“Bon-ferronization”) and, thereby, to unduly increased risks of not rejecting false hypotheses. In general, analytical models adequate for such complex data structures and suitable for practical use do not exist as yet. Exploratory Data Analysis (EDA), on the other hand, is usually intended to generate hypotheses and not to lead to final conclusions based on the results of the study.In this paper, it is proposed to fill the conceptual gap between CDA and EDA by “Descriptive Data Analysis” (“DDA”) which concept is mainly based on descriptive inferential statements. The results of a DDA in a controlled study are interpreted simultaneously on the basis of the investigator’s experience with respect to numerically relevant treatment effect differences and on “descriptive significances” as they appear in “near regular” patterns corresponding to the resulting relevant effect differences. A DDA may also contain confirmatory parts and/or tests on global hypotheses at a prechosen maximum risk α of erroneously rejecting true hypotheses. The paper is in parts expository and is addressed to investigators as well as statisticians.

scholarly journals Current exploratory and confirmatory issues in data analysis in psychology

2010 ◽  
Vol 3 (1) ◽  
pp. 4-8
Author(s):  
Fernando Marmolejo-Ramos
Keyword(s):  
Data Analysis ◽  
San Francisco ◽  
Exploratory Data Analysis ◽  
American Psychological Association ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
American Psychological ◽  
Exploratory Data ◽  
Confirmatory Data Analysis ◽  
Analytical Tools

In 1968 John Tukey gave a speech at the American Psychological Association in San Francisco about the relevance of proper data analysis in Psychology (Tukey, 1969). His closing message was that “data analysis needs to be both exploratory and confirmatory” (p. 90). Exploratory data analysis (or EDA) is an approach to analysing data in order to formulate sound hypotheses, whereas confirmatory data analysis (CDA) is a method to test those hypotheses (a.k.a., statistical hypothesis testing). As Tukey announced in his speech, these two analytical tools have been, and are somewhat still, at odds. This special issue presents sixteen papers that cover relevant topics in EDA and CDA with the purpose of bringing together seemingly disparate issues.

scholarly journals Gender and Ultimatum in Pakistan: Revisited

2014 ◽  
Vol 53 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Saima Naeem ◽  
Asad Zaman
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Ultimatum Game ◽  
Original Data ◽  
Jel Classification ◽  
Data Set ◽  
Analysis Techniques ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

Razzaque (2009) studied the role of gender in the ultimatum game by running experiments on students in various cities in Pakistan. He used standard confirmatory data analysis techniques, which work well in familiar contexts, where relevant hypotheses of interest are known in advance. Our goal in this paper is to demonstrate that exploratory data analysis is much better suited to the study of experimental data where the goal is to discover patterns of interest. Our exploratory re-analysis of the original data set of Razzaque (2009) leads to several new insights. While we re-confirm the main finding of Razzaque regarding the greater generosity of males, additional analysis suggests that this is driven by student subculture in Pakistan, and would not generalise to the population at large. In addition, we find strong effect of urbanisation. Our exploratory data analysis also offers considerable additional insights into the learning process that takes place over the course of a sequence of games. JEL Classification: C78, C81, C91, J16 Keywords: Ultimatum Game, Gender Differences, Exploratory Data Analysis

Microarrays, pattern recognition and exploratory data analysis

2003 ◽  
Vol 22 (11) ◽  
pp. 1879-1899 ◽  
Author(s):  
Bart J. A. Mertens
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Exploratory Data

Comparison of two exploratory data analysis methods for classification of Phyllanthus chemical fingerprint: unsupervised vs. supervised pattern recognition technologies

2014 ◽  
Vol 407 (5) ◽  
pp. 1389-1401 ◽  
Author(s):  
Jianru Guo ◽  
QianQian Chen ◽  
Caiyun Wang ◽  
Hongcong Qiu ◽  
Buming Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Chemical Fingerprint ◽  
Analysis Methods ◽  
Exploratory Data ◽  
Data Analysis Methods

scholarly journals CHALLENGE-STRESS AND HINDRANCE-STRESS IN THE SOUTHEAST ASIAN HOTEL SECTOR

2016 ◽  
Vol 29 (3) ◽  
Author(s):  
Kamaazura Abu Abu ◽  
Ahmad Munir Mohd Salleh ◽  
Mohd Shaladdin Muda ◽  
Azlinzuraini Ahmad ◽  
Ruzita Manshor
Keyword(s):  
Data Analysis ◽  
Workplace Stress ◽  
Working Environment ◽  
Stress Factors ◽  
Food And Beverage ◽  
Hospitality Organizations ◽  
Challenge Stress ◽  
Hindrance Stress ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

The hospitality industry is an entity that is continuously determined by varying new demands and the needs of its customers. This ever-changing and complex working environment has caused and become a source of stress for the hospitality industries’ workforce. Workplace stress is increasing from year to year and has become a focus of research interest in recent years.Responding to the demands of management who require a more precise understanding of the issues of workplace stress, researchers have conducted studies on a total of 115 respondents from a 3 star-hotel and a 4 star-hotel. The personnel involved came from the food and beverage departments, room services and the front offices, whose daily routines involved direct face toface serving activities and fulfilling their customers’ demands. Using the Statistical Package for Social Science (SPSS) version 19.0 and AMOS version 18.0, the results of Exploratory Data Analysis (EFA) and Confirmatory Data Analysis (CFA) have confirmed that there are two stress factors, namely challenge stress and hindrance stress. Both of these stress factors have asignificantly negative relation to one another. Understanding these dimensions in detail can help the hospitality organizations to be well prepared for the task of motivating their employees.Keywords: challenge-stress, hindrance-stress, service, Southeast Asia, hotel

Sign in / Sign up

Export Citation Format

Share Document