Machine Learning Applications in Rough Set Theory

2010 ◽  
Author(s):  
Wenshan Wei ◽  
Haihua Li
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Applications

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.

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

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.

scholarly journals Application of machine learning and rough set theory in lean maintenance decision support system development

2021 ◽  
Vol 23 (4) ◽  
pp. 695-708
Author(s):  
Katarzyna Antosz ◽  
Małgorzata Jasiulewicz-Kaczmarek ◽  
Łukasz Paśko ◽  
Chao Zhang ◽  
Shaoping Wang
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
System Development ◽  
Decision Makers ◽  
Manufacturing Companies ◽  
New Approach ◽  
Structured Approach ◽  
Selection Of

Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.

Rough Sets Based Rule Generation from Data with Categorical and Numerical Values

2008 ◽  
Vol 12 (5) ◽  
pp. 426-434 ◽  
Author(s):  
Hiroshi Sakai ◽  
◽  
Kazuhiro Koba ◽  
Michinori Nakata ◽  
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Rule Generation ◽  
Utility Program ◽  
New Framework

Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.

scholarly journals A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

2019 ◽  
Vol 11 (23) ◽  
pp. 6803
Author(s):  
Jiwoo Kim ◽  
Sanghun Shin ◽  
Hee Soo Lee ◽  
Kyong Joo Oh
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Initial Public Offering ◽  
Rough Set ◽  
Rough Set Theory ◽  
Investment Strategy ◽  
Portfolio Allocation ◽  
Individual Investors ◽  
Rule Sets ◽  
Public Offering

An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, studies on portfolio allocation strategies for IPO stocks are relatively scarce. This paper develops a machine learning investment strategy for IPO stocks based on rough set theory and a genetic algorithm (GA-rough set theory). To reduce issues of information asymmetry, we use nonfinancial data that are publicly available to individual and institutional investors in the IPO process. Based on the rule sets generated from the training sets, we conduct 120 tests with various conditions involving the target days and the partition of the training and testing sets, and we find excess returns of the constructed portfolios compared to the benchmark portfolios. Investors in IPO stocks can formulate more efficient investment strategies using our system. In this sense, the system developed in this paper contributes to the efficiency of financial markets and helps achieve sustained economic growth.

scholarly journals Supervised Machine Learning in Electrofacies Classification: A Rough Set Theory Approach

2020 ◽  
Vol 1529 ◽  
pp. 052048
Author(s):  
Touhid Mohammad Hossain ◽  
Junzo Wataada ◽  
Maman Hermana ◽  
Izzatdin A Aziz
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Supervised Machine Learning ◽  
Theory Approach

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.

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