Knowledge Acquisition of Interval Set-Valued Based on Granular Computing

2014 ◽  
Vol 543-547 ◽  
pp. 2017-2023
Author(s):  
Qing Guan ◽  
Jian He Guan
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Fuzzy Rough Set ◽  
Decision Systems ◽  
Machine Learning Methods ◽  
Rough Theory

The technique of a new extension of fuzzy rough theory using partition of interval set-valued is proposed for granular computing during knowledge discovery in this paper. The natural intervals of attribute values in decision system to be transformed into multiple sub-interval of [0,1]are given by normalization. And some characteristics of interval set-valued of decision systems in fuzzy rough set theory are discussed. The correctness and effectiveness of the approach are shown in experiments. The approach presented in this paper can also be used as a data preprocessing step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

An Expanded Feature Extraction of E-Mail Header for Spam Recognition

2013 ◽  
Vol 846-847 ◽  
pp. 1672-1675 ◽  
Author(s):  
Yuan Ning Liu ◽  
Ye Han ◽  
Xiao Dong Zhu ◽  
Fei He ◽  
Li Yan Wei
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Spam Filtering ◽  
Feature Sets ◽  
Filtering Method ◽  
The Past ◽  
Machine Learning Methods ◽  
E Mail

Currently a spam filtering method is extracting attributes from e-mail header and using machine learning methods to classify the sample sets. But as time goes on, spammers transform different ways to send spam, which result in a great change of spam's header. So the attributes defined in the past could not deal with this change sufficiently. This paper extracted attributes from all possible forged header fields to expand the feature sets, then used the rough set theory to classify the sample sets. Experiment validated more attributes including in feature sets may lead to greater performance, in terms of higher recall and precision, lower fake recognition than other algorithms.

Probability approach for interval-valued ordered decision systems in dominance-based fuzzy rough set theory

2017 ◽  
Vol 32 (1) ◽  
pp. 703-710 ◽  
Author(s):  
Jianhua Dai ◽  
Guojie Zheng ◽  
Huifeng Han ◽  
Qinghua Hu ◽  
Nenggan Zheng ◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fuzzy Rough Set ◽  
Decision Systems ◽  
Probability Approach ◽  
Interval Valued

Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

2015 ◽  
Vol 142 (1-4) ◽  
pp. 53-86 ◽  
Author(s):  
Sarah Vluymans ◽  
Lynn D’eer ◽  
Yvan Saeys ◽  
Chris Cornelis
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fuzzy Rough Set

scholarly journals Some Properties Related to Reduct of Consistent Decision Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 3-9
Author(s):  
Nguyen Long Giang ◽  
Demetrovics Janos ◽  
Vu Duc Thi ◽  
Phan Dang Khoa
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Heuristic Algorithms ◽  
Decision Systems ◽  
The Family ◽  
Complete Study ◽  
Sperner System ◽  
Classification Quality

Abstract Reduct of decision systems is the topic that has been attracting the interest of many researchers in data mining and machine learning for more than two decades. So far, many algorithms for finding reduct of decision systems by rough set theory have been proposed. However, most of the proposed algorithms are heuristic algorithms that find one reduct with the best classification quality. The complete study of properties of reduct of decision systems is limited. In this paper, we discover equivalence properties of reduct of consistent decision systems related to a Sperner-system. As the result, the study of the family of reducts in a consistent decision system is the study of Sperner-systems.

Rough Sets and Granular Computing in Geospatial Information

2010 ◽  
pp. 154-159
Author(s):  
Iftikhar U. Sikder
Keyword(s):  
Knowledge Discovery ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Granular Computing ◽  
Spatial Reasoning ◽  
Rough Set Theory ◽  
Geospatial Information

The representation of geographic entities is characterized by inherent granularity due to scale and resolution specific observations. This article discusses the various aspects of rough set-based approximation modeling of spatial and conceptual granularity. It outlines the context and applications of rough set theory in representing objects with intermediate boundaries, spatial reasoning and knowledge discovery.

THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY

2004 ◽  
Vol 12 (01) ◽  
pp. 37-46 ◽  
Author(s):  
JIYE LIANG ◽  
ZHONGZHI SHI
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Computer Applications ◽  
Mathematical Tool

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, rough entropy and knowledge granulation in rough set theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in rough set theory.

An approach to evaluation of emergency plans for unconventional emergency events based on soft fuzzy rough set

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 461-473 ◽  
Author(s):  
Sun Bingzhen ◽  
Ma Weimin
Keyword(s):  
Decision Making ◽  
Emergency Management ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Management Decision ◽  
Fuzzy Rough Set ◽  
Emergency Plans ◽  
Content Type ◽  
Emergency Events

Purpose – The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology. Design/methodology/approach – In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory. Findings – This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events. Originality/value – The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

scholarly journals A rough set theory and deep learning based predictive system for gender recognition using audio speech

2021 ◽  
Author(s):  
Ghazaala Yasmin ◽  
ASIT KUMAR DAS ◽  
Janmenjoy Nayak ◽  
S Vimal ◽  
Soumi Dutta
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Set Theory ◽  
Rough Set ◽  
Gender Discrimination ◽  
Rough Set Theory ◽  
Recognition System ◽  
Gender Recognition ◽  
Learning Techniques

Abstract Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

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