scholarly journals Random Forest Classifier based on Variable Precision Rough Set Theory

2017 ◽  
Vol 169 (9) ◽  
pp. 1-5 ◽  
Author(s):  
Subi Jain ◽  
Gagan Vishwakarma ◽  
Yogendra Kumar
Keyword(s):  
Random Forest ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Random Forest Classifier ◽  
Variable Precision ◽  
Variable Precision Rough Set

scholarly journals Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

2012 ◽  
Vol 9 (3) ◽  
pp. 1-17 ◽  
Author(s):  
D. Calvo-Dmgz ◽  
J. F. Gálvez ◽  
D. Glez-Peña ◽  
S. Gómez-Meire ◽  
F. Fdez-Riverola
Keyword(s):  
Gene Expression ◽  
Set Theory ◽  
Rough Set ◽  
Microarray Data ◽  
Rough Set Theory ◽  
Biological Knowledge ◽  
Variable Precision ◽  
Gene Sets ◽  
Proposed Model ◽  
Variable Precision Rough Set

Summary DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

An Exposition of Feature Selection and Variable Precision Rough Set Analysis

2010 ◽  
pp. 193-213
Author(s):  
Malcolm J. Beynon ◽  
Benjamin Griffiths
Keyword(s):  
Soft Computing ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Credit Ratings ◽  
Decision Rules ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Variable Precision Rough Sets

This chapter considers, and elucidates, the general methodology of rough set theory (RST), a nascent approach to rule based classification associated with soft computing. There are two parts of the elucidation undertaken in this chapter, firstly the levels of possible pre-processing necessary when undertaking an RST based analysis, and secondly the presentation of an analysis using variable precision rough sets (VPRS), a development on the original RST that allows for misclassification to exist in the constructed “if … then …” decision rules. Throughout the chapter, bespoke software underpins the pre-processing and VPRS analysis undertaken, including screenshots of its output. The problem of US bank credit ratings allows the pertinent demonstration of the soft computing approaches described throughout.

scholarly journals A Variable Precision Covering-Based Rough Set Model Based on Functions

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yanqing Zhu ◽  
William Zhu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Misclassification Rate ◽  
Extended Model ◽  
Variable Precision ◽  
Rate Functions ◽  
Variable Precision Rough Set

Classical rough set theory is a technique of granular computing for handling the uncertainty, vagueness, and granularity in information systems. Covering-based rough sets are proposed to generalize this theory for dealing with covering data. By introducing a concept of misclassification rate functions, an extended variable precision covering-based rough set model is proposed in this paper. In addition, we define thef-lower andf-upper approximations in terms of neighborhoods in the extended model and study their properties. Particularly, two coverings with the same reductions are proved to generate the samef-lower andf-upper approximations. Finally, we discuss the relationships between the new model and some other variable precision rough set models.

A New Criterion for Attribute Reduction Based on Variable Precision Rough Set Model

2011 ◽  
Vol 121-126 ◽  
pp. 1579-1584
Author(s):  
Hai Zhong Tan
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Variable Precision ◽  
Rule Set ◽  
Variable Precision Rough Set ◽  
New Criterion

The rule set which is acquired based on rough set theory can be classified into two categories: deterministic rules and probabilistic rules. Traditional attribute reduction definitions in variable precision rough set model cannot guarantee the rule properties, namely deterministic or probabilistic. In this paper, a new criterion for attribute reduction is put forward based on variable precision rough set model. The rule properties can be preserved during the process of attribute reduction. The relationships between the new reduct definition and available definitions, including Ziarko’s reduct definition and β lower distribution reduct definition are also discussed.

scholarly journals Variable precision rough set model for attribute selection on environment impact dataset

2018 ◽  
Vol 4 (1) ◽  
pp. 70
Author(s):  
Ani Apriani ◽  
Iwan Tri Riyadi Yanto ◽  
Septiana Fathurrohmah ◽  
Sri Haryatmi ◽  
Danardono Danardono
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Computational Models ◽  
Open Space ◽  
Rough Set Theory ◽  
Policy Design ◽  
Attribute Selection ◽  
Environment Impact ◽  
Variable Precision ◽  
Variable Precision Rough Set

The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment.

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