Inexact Field Learning Approach for Data Mining

2011 ◽  
pp. 611-614
Author(s):  
Honghua Dai
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Quality Data ◽  
Learning Approach ◽  
Individual Values ◽  
Machine Learning Approach

Inexact fielding learning (IFL) (Ciesieski & Dai, 1994; Dai & Ciesieski, 1994a, 1994b, 1995, 2004; Dai & Li, 2001) is a rough-set, theory-based (Pawlak, 1982) machine learning approach that derives inexact rules from fields of each attribute. In contrast to a point-learning algorithm (Quinlan, 1986, 1993), which derives rules by examining individual values of each attribute, a field learning approach (Dai, 1996) derives rules by examining the fields of each attribute. In contrast to exact rule, an inexact rule is a rule with uncertainty. The advantage of the IFL method is the capability to discover high-quality rules from low-quality data, its property of low-quality data tolerant (Dai & Ciesieski, 1994a, 2004), high efficiency in discovery, and high accuracy of the discovered rules.

Inexact Field Learning Approach for Data Mining

2011 ◽  
pp. 1019-1022
Author(s):  
Honghua Dai
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Quality Data ◽  
Learning Approach ◽  
Individual Values ◽  
Machine Learning Approach

Inexact fielding learning (IFL) (Ciesieski & Dai, 1994; Dai & Ciesieski, 1994a, 1994b, 1995, 2004; Dai & Li, 2001) is a rough-set, theory-based (Pawlak, 1982) machine learning approach that derives inexact rules from fields of each attribute. In contrast to a point-learning algorithm (Quinlan, 1986, 1993), which derives rules by examining individual values of each attribute, a field learning approach (Dai, 1996) derives rules by examining the fields of each attribute. In contrast to exact rule, an inexact rule is a rule with uncertainty. The advantage of the IFL method is the capability to discover high-quality rules from low-quality data, its property of low-quality data tolerant (Dai & Ciesieski, 1994a, 2004), high efficiency in discovery, and high accuracy of the discovered rules.

scholarly journals English poems categorization using text mining and rough set theory

2020 ◽  
Vol 9 (4) ◽  
pp. 1701-1710
Author(s):  
Saif Ali Alsaidi ◽  
Ahmed T. Sadeq ◽  
Hasanen S. Abdullah
Keyword(s):  
Text Mining ◽  
Set Theory ◽  
Rough Set ◽  
Text Categorization ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Text Data ◽  
Data Set ◽  
Proposed Model

In recent years, Text Mining wasan important topic because of the growth of digital text data from many sources such as government document, Email, Social Media, Website, etc. The English poemsare one of the text data to categorization English Poems will use Text categorization, Text categorization is a method in which classify documents into one or more categories that were predefined the category based on the text content in a document .In this paper we will solve the problem of how to categorize the English poem into one of the English Poems categorizations by using text mining technique and Machine learning algorithm, Our data set consist of seven categorizations for poems the data set is divided into two-part training (learning)and testing data. In the proposed model we apply the text preprocessing for the documents file to reduce the number of feature and reduce dimensionality the preprocessing process converts the text poem to features and remove the irrelevant feature by using text mining process (tokenize,remove stop word and stemming), to reduce the feature vector of the remaining feature we usetwo methods for feature selection and use Rough set theory as machine learning algorithm to perform the categorization, and we get 88% success classification of the proposed model.

Analysis and assessment of confined and phreatic water quality using a rough set theory method in Jilin City, China

2015 ◽  
Vol 15 (4) ◽  
pp. 773-783 ◽  
Author(s):  
He Huang ◽  
Xiujuan Liang ◽  
Changlai Xiao ◽  
Zhong Wang
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Pore Water ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Quality Data ◽  
Confined Water ◽  
Permissible Limits ◽  
Phreatic Water

In groundwater quality assessments it is easier and more effective to reduce the number of parameters included in water quality indices. A total of 20 quaternary loose rock pore water and tertiary clastic rock cranny pore water data sets were used for Jilin City, China, as basic data, and 10 water quality parameters were selected for reduction using rough set theory and a statistical analysis of groundwater quality. Results showed that the quality of confined water was better than that of phreatic water in the study area. Confined water was of good quality, and met the permissible limits of the Quality Standards for Groundwater of China, with the exception of NH4+ and F−. For phreatic water, the five parameters of total dissolved solids, NH4+, NO2−, Fe, and F− exceeded the permissible limits, with levels of NH4+ and Fe having a 70% and 40% rate of exceedance, respectively. The results indicated that water evaluation before and after attribute reduction was consistent, which suggests that through rough set theory redundant parameters in indices were eliminated but the accuracy of water quality classification remained effective, while the complexity of the calculation was reduced. Rough set theory provides a convenient and appropriate way to manage large amounts of water quality data.

scholarly journals The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises

2020 ◽  
Vol 10 (21) ◽  
pp. 7922
Author(s):  
Katarzyna Antosz ◽  
Lukasz Pasko ◽  
Arkadiusz Gola
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Trees ◽  
Set Theory ◽  
Rough Set ◽  
Intelligent Systems ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Conservation Strategy ◽  
Artificial Intelligence Methods

The increase in the performance and effectiveness of maintenance processes is a continuous aim of production enterprises. The elimination of unexpected failures, which generate excessive costs and production losses, is emphasized. The elements that influence the efficiency of maintenance are not only the choice of an appropriate conservation strategy but also the use of appropriate methods and tools to support the decision-making process in this area. The research problem, which was considered in the paper, is an insufficient means of assessing the degree of the implementation of lean maintenance. This problem results in not only the possibility of achieving high efficiency of the exploited machines, but, foremost, it influences a decision process and the formulation of maintenance policy of an enterprise. The purpose of this paper is to present the possibility of using intelligent systems to support decision-making processes in the implementation of the lean maintenance concept, which allows the increase in the operational efficiency of the company’s technical infrastructure. In particular, artificial intelligence methods were used to search for relationships between specific activities carried out under the implementation of lean maintenance and the results obtained. Decision trees and rough set theory were used for the analysis. The decision trees were made for the average value of the overall equipment effectiveness (OEE) indicator. The rough set theory was used to assess the degree of utilization of the lean maintenance strategy. Decision rules were generated based on the proposed algorithms, using RSES software, and their correctness was assessed.

scholarly journals Application of Rough Set Theory to Water Quality Analysis: A Case Study

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 50 ◽  
Author(s):  
Maryam Zavareh ◽  
Viviana Maggioni
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Specific Conductivity ◽  
Quality Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Indicators

This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the George Mason University campus in Fairfax, VA during three years (October 2015–December 2017). Rough set theory is applied to monthly averages of the collected data to estimate one indicator (decision attribute) based on the remainder indicators and to determine what indicators (conditional attributes) are essential (core) to predict the missing indicator. The redundant attributes are identified, the importance degree of each attribute is quantified, and the certainty and coverage of any detected rule(s) is evaluated. Possible decision making rules are also assessed and the certainty coverage factor is calculated. Results show that the core water quality indicators for the Mason watershed during the study period are turbidity and specific conductivity. Particularly, if pH is chosen as a decision attribute, the importance degree of turbidity is higher than the one of conductivity. If the decision attribute is turbidity, the only indispensable attribute is specific conductivity and if specific conductivity is the decision attribute, the indispensable attribute beside turbidity is temperature.

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