Rough Set Approach in Machine Learning: A Review (original) (raw)

Rough Set Theory Approach in Feature Selection and Clustering

2018

Abstract—The Rough Set (RS) theory may be considered as a tool to reduce the input spatial property and to influence unclearness and uncertainty in datasets. Over the years, there has been arapid growth in interest in rough set theory and its applications in computer science and cognitive sciences,especially in analysis areas like machine learning,intelligent systems, colligation, pattern recognition,data pre-processing, data discovery, decision analysis,and knowledgeable systems. This paper discusses the fundamental ideas of rough pure mathematics and imply some rough set-based analysis directions and applications. The discussion additionally includes are view of rough set theory in numerous machine learningtechniques like clump, feature choice and rule induction.

Application of Rough Set Theory in Data Mining

Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. Data mining is a discipline that has an important contribution to data analysis, discovery of new meaningful knowledge, and autonomous decision making. The rough set theory offers a viable approach for decision rule extraction from data.This paper, introduces the fundamental concepts of rough set theory and other aspects of data mining, a discussion of data representation with rough set theory including pairs of attribute-value blocks, information tables reducts, indiscernibility relation and decision tables. Additionally, the rough set approach to lower and upper approximations and certain possible rule sets concepts are introduced. Finally, some description about applications of the data mining system with rough set theory is included.

Rough Set : Buzzword of Data Classification

2016

Classification is an important Data Mining Technique with broad applications in every walk of life. It is termed as classifying each item in a set of data into one of predefined set of classes or groups. The present study compares the performance evaluation of Naïve Bayes, Random Forest, k Star, Multilayer Preceptron, j48 classification algorithm and Rough Set Theory. The paper presents the experimental results about classification accuracy and explores that the accuracy of Rough Set Theory is improved than other

Rough Set Algorithms in Classification Problem

Studies in Fuzziness and Soft Computing, 2000

In the paper we present some algorithms, based on rough set theory, that can be used for the problem of new cases classification. Most of the algorithms were implemented and included in Rosetta system [43]. We present several methods for computation of decision rules based on reducts. We discuss the problem of real value attribute discretization for increasing the performance of algorithms and quality of decision rules. Finally we deal with a problem of resolving conflicts between decision rules classifying a new case to different categories (classes).

Applications of rough sets theory in data preprocessing for knowledge discovery

Proceedings of the World Congress on …, 2007

Data preprocessing is a step of the Knowledge discovery in databases (KDD) process that reduces the complexity of the data and offers better conditions to subsequent analysis. Rough sets theory, where sets are approximated using elementary sets, is a different approach for developing methods for the data preprocessing process. In this paper Rough sets theory is applied to three preprocessing steps: Discretization, Feature selection, and instance selection. The new methods proposed in this paper have been tested on eight datasets widely used in the KDD community.

New learning models for generating classification rules based on rough set approach

2000

Data sets, static or dynamic, are very important and useful for presenting real life features in different aspects of industry, medicine, economy, and others. Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzz y logic, genetic algorithm, rough set theory, and others. All of these models take long time to learn for a huge and dynamic data set. Thus, the challenge is how to develop an efficient model that can decrease the learning time without affecting the quality of the generated classification rules. Huge information systems or data sets usually have some missing values due to unavailable data that affect the quality of the generated classification rules. Missing values lead to the difficulty of extracting useful information from that data set. Another challenge is how to solve the problem of missing data.

On applications of rough sets theory to knowledge discovery

2008

Knowledge Discovery in Databases (KDD) is the nontrivial extraction of implicit, previously unknown and potentially useful information from data. Data preprocessing is a step of the KDD process that reduces the complexity of the data and offers better conditions to subsequent analysis. Rough sets theory, where sets are approximated using elementary sets, is another approach for developing methods for the KDD process. In this doctoral Thesis, we propose new algorithms based on Rough sets theory for three data preprocessing steps: Discretization, feature selection, and instance selection. In Discretization, continuous features are transformed into new categorical features. This is required for some KDD algorithms working strictly with categorical features. In Feature selection, the new subset of features leads to a new dataset of lower dimension, where it is easier to perform a KDD task. When a dataset is very large, an instance selection process is required to decrease the computatio...

A Rough Sets-Based Rule Induction for Numerical Datasets

2018

The design of interpretable classifiers is a major goal in machine learning since many applications rely on a complete understanding of the learning model decisions. Among all interpretable models available in the literature, Rough sets based models have become popular given the capability of rough sets to model imprecise data. Despite its success, some of the most used rough sets models are designed to work for categorical input data. Since this design choice may severely limit the application of such models in real world problems, in this paper, we aim at showing some strategies to enable rough sets based models to work with numerical datasets.