Tolerance rough set firefly-based quick reduct

2016 ◽  
Vol 28 (10) ◽  
pp. 2995-3008 ◽  
Author(s):  
Jothi Ganesan ◽  
Hannah H. Inbarani ◽  
Ahmad Taher Azar ◽  
Kemal Polat
Keyword(s):  
Rough Set ◽  
Tolerance Rough Set ◽  
Quick Reduct

Hybrid Tolerance Rough Set

2013 ◽  
Vol 3 (4) ◽  
pp. 15-30 ◽  
Author(s):  
G. Jothi ◽  
H. Hannah Inbarani ◽  
Ahmad Taher Azar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Diagnostic Accuracy ◽  
Rough Set ◽  
Information Loss ◽  
Discretization Method ◽  
Data Set ◽  
Tolerance Rough Set ◽  
Mammogram Images ◽  
Quick Reduct

Breast cancer is the most common malignant tumor found among young and middle aged women. Feature Selection is a process of selecting most enlightening features from the data set which preserves the original significance of the features following reduction. The traditional rough set method cannot be directly applied to deafening data. This is usually addressed by employing a discretization method, which can result in information loss. This paper proposes an approach based on the tolerance rough set model, which has the flair to deal with real-valued data whilst simultaneously retaining dataset semantics. In this paper, a novel supervised feature selection in mammogram images, using Tolerance Rough Set - PSO based Quick Reduct (STRSPSO-QR) and Tolerance Rough Set - PSO based Relative Reduct (STRSPSO-RR), is proposed. The results obtained using the proposed methods show an increase in the diagnostic accuracy.

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.

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
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