3 research outputs found
Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection
This study proposes an alternate data extraction method that combines three well-known
feature selection methods for handling large and problematic datasets: the correlation-based feature
selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods.
This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several
phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks.
Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve
the computational time complexity and increase the classification accuracy. Several datasets with
various characteristics and volumes were used in the experimental process to evaluate the proposed
method’s credibility. The method’s performance was validated using standard evaluation measures
and benchmarked with other established methods such as deep learning (DL). Overall, the proposed
work proved that it could assist the classifier in returning a significant result, with an accuracy rate
of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM),
which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical
result indicates that the proposed method is an alternative extraction tool for those with difficulties
acquiring expensive big data analysis tools and those who are new to the data analysis field.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty
of Informatics and Management, Czech Republic (ID: 2102–2021), “Smart Solutions in Ubiquitous
Computing Environments
Intuitionistic Fuzzy Soft Rough Set and Its Application in Decision Making
The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. In this paper, we present concepts of soft rough intuitionistic fuzzy sets and intuitionistic fuzzy soft rough sets, and investigate some properties of soft rough intuitionistic fuzzy sets and intuitionistic fuzzy soft rough sets in detail. Furthermore, classical representations of intuitionistic fuzzy soft rough approximation operators are presented. Finally, we develop an approach to intuitionistic fuzzy soft rough sets based on decision making and a numerical example is provided to illustrate the developed approach