scholarly journals An Incremental Learning Algorithm Based on Rough Set Theory

2007 ◽  
pp. 444-447 ◽  
Author(s):  
Yinghong Ma ◽  
Yehong Han
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Incremental Learning ◽  
Rough Set Theory ◽  
Learning Algorithm

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.

Incremental learning for text categorization using rough set boundary based optimized Support Vector Neural Network

2020 ◽  
Vol 54 (5) ◽  
pp. 585-601
Author(s):  
N. Venkata Sailaja ◽  
L. Padmasree ◽  
N. Mangathayaru
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Incremental Learning ◽  
Text Categorization ◽  
Rough Set Theory ◽  
Distance Measure ◽  
Support Vector ◽  
Content Type

PurposeText mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information.Design/methodology/approachThe primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input.FindingsFor the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F-measure, respectively.Originality/valueIn this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.

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.

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.

Incremental Learning Researches on Rough Set Theory

2014 ◽  
Vol 1 (1) ◽  
pp. 99-112 ◽  
Author(s):  
Dun Liu ◽  
Decui Liang
Keyword(s):  
Learning Strategies ◽  
Set Theory ◽  
Rough Set ◽  
Incremental Learning ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Dynamic Environment ◽  
Future Research ◽  
Research Directions ◽  
The Status

Rough set theory is an effective tool to deal with information with uncertainty, and has been successfully applied in many fields. Incremental learning as an efficient strategy for data analysis in dynamic environment enables acquiring additional knowledge from new information by using prior knowledge and has drawn the widespread attentions of many scholars. In this paper, the authors discuss the status of incremental learning researches on rough sets and give potential future research directions. The authors first review basic concepts of rough sets and list three variations of information system in the dynamic decision procedures. Then, the authors investigate and summarize the corresponding incremental learning strategies for the three variations with different research viewpoints, respectively. Finally, the authors further tease out the research framework of our work and identify some future possible research directions.

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