Weight Learning for Document Tolerance Rough Set Model

2013 ◽  
pp. 385-396 ◽  
Author(s):  
Wojciech Świeboda ◽  
Michał Meina ◽  
Hung Son Nguyen
Keyword(s):  
Rough Set ◽  
Tolerance Rough Set ◽  
Weight Learning

A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection

2013 ◽  
Vol 2 (4) ◽  
pp. 33-46 ◽  
Author(s):  
P. K. Nizar Banu ◽  
H. Hannah Inbarani
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Gene Selection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Huge Number ◽  
Number Of Genes ◽  
Micro Array ◽  
Tolerance Rough Set ◽  
Array Databases

As the micro array databases increases in dimension and results in complexity, identifying the most informative genes is a challenging task. Such difficulty is often related to the huge number of genes with very few samples. Research in medical data mining addresses this problem by applying techniques from data mining and machine learning to the micro array datasets. In this paper Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR), a diverse feature selection algorithm, which extends the existing equivalent rough sets for unsupervised learning, is proposed. Genes selected by the proposed method leads to a considerably improved class predictions in wide experiments on two gene expression datasets: Brain Tumor and Colon Cancer. The results indicate consistent improvement among 12 classifiers.

Learning relational facts from the web: A tolerance rough set approach

2015 ◽  
Vol 67 ◽  
pp. 130-137 ◽  
Author(s):  
Cenker Sengoz ◽  
Sheela Ramanna
Keyword(s):  
Rough Set ◽  
Tolerance Rough Set ◽  
The Web

scholarly journals Sentence Similarity Calculation Based on Probabilistic Tolerance Rough Sets

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ruiteng Yan ◽  
Dong Qiu ◽  
Haihuan Jiang
Keyword(s):  
Language Processing ◽  
Rough Set ◽  
Time Complexity ◽  
Computation Model ◽  
Text Data ◽  
Efficient Performance ◽  
Sentence Similarity ◽  
Similarity Calculation ◽  
Tolerance Rough Set ◽  
Similarity Computation

Sentence similarity calculation is one of the important foundations of natural language processing. The existing sentence similarity calculation measurements are based on either shallow semantics with the limitation of inadequately capturing latent semantics information or deep learning algorithms with the limitation of supervision. In this paper, we improve the traditional tolerance rough set model, with the advantages of lower time complexity and becoming incremental compared to the traditional one. And then we propose a sentence similarity computation model from the perspective of uncertainty of text data based on the probabilistic tolerance rough set model. It has the ability of mining latent semantics information and is unsupervised. Experiments on SICK2014 task and STSbenchmark dataset to calculate sentence similarity identify a significant and efficient performance of our model.

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

2017 ◽  
Vol 13 (4) ◽  
pp. 38-55 ◽  
Author(s):  
Han Ke
Keyword(s):  
Breast Cancer ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Network Structure ◽  
High Efficiency ◽  
Breast Cancer Dataset ◽  
Intelligent Technique ◽  
Tolerance Rough Set ◽  
Learning Machine

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

2020 ◽  
pp. 263-282
Author(s):  
Han Ke
Keyword(s):  
Breast Cancer ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Network Structure ◽  
High Efficiency ◽  
Breast Cancer Dataset ◽  
Tolerance Rough Set ◽  
Learning Machine ◽  
Lower And Upper Approximations

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

A Similarity Rough Set Model for Document Representation and Document Clustering

2011 ◽  
Vol 15 (2) ◽  
pp. 125-133 ◽  
Author(s):  
Nguyen Chi Thanh ◽  
◽  
Koichi Yamada ◽  
Muneyuki Unehara
Keyword(s):  
Vector Space ◽  
Rough Set ◽  
Document Clustering ◽  
Vector Space Model ◽  
Document Representation ◽  
Space Model ◽  
Large Sets ◽  
Tolerance Rough Set ◽  
Document Organization ◽  
The One

Document clustering is a textmining technique for unsupervised document organization. It helps the users browse and navigate large sets of documents. Ho et al. proposed a Tolerance Rough Set Model (TRSM) [1] for improving the vector space model that represents documents by vectors of terms and applied it to document clustering. In this paper we analyze their model to propose a new model for efficient clustering of documents. We introduce Similarity Rough Set Model (SRSM) as another model for presenting documents in document clustering. The model is evaluated by experiments on test collections. The experiment results show that the SRSM document clusteringmethod outperforms the one with TRSM and the results of SRSM are less affected by the value of parameter than TRSM.

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