Paper Info
Title
A Comparative Study of Statistical and Rough Computing Models in Predictive Data Analysis.
Abstract
Information and technology revolution has brought a radical change in the way data are collected. The data collected is of no use unless some useful information is derived from it. Therefore, it is essential to think of some predictive analysis for analyzing data and to get meaningful information. Much research has been carried out in the direction of predictive data analysis starting from statistical techniques to intelligent computing techniques and further to hybridize computing techniques. The prime objective of this paper is to make a comparative analysis between statistical, rough computing, and hybridized techniques. The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA. It is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1% as compared to rough computing techniques.
Year
DOI
Venue
2017
10.4018/IJACI.2017040103
IJACI
Keywords
Field
DocType
Almost Indiscernibility, Correlation, Equivalence Class, Fuzzy Proximity Relation, Fuzzy Relation, Mean Percentile Error, Mean Square Error, Neural Network, Prediction, Regression Analysis, Rough Set
Prime (order theory),Data mining,Intelligent computing,Computer science,Regression analysis,Mean squared error,Rough set,Artificial intelligence,Equivalence class,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
8
2
1941-6237
Citations 
PageRank 
References 
13
0.62
8
Authors
2
Name
Order
Citations
PageRank
D. P. Acharjya1608.98
A. Anitha2161.33