Data mining: concepts and techniques
Data mining: concepts and techniques
Rough set algorithms in classification problem
Rough set methods and applications
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Machine Learning
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multi-layer Perceptrons for Functional Data Analysis: A Projection Based Approach
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Evaluating students' learning achievement using fuzzy membership functions and fuzzy rules
Expert Systems with Applications: An International Journal
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
Corporate memory in the ecotourism: a rough set base
Proceedings of the 14th Annual International Conference on Electronic Commerce
Supervised learning approaches and feature selection - a case study in diabetes
International Journal of Data Analysis Techniques and Strategies
Hi-index | 12.07 |
In strategy of investment, an important thing for investors is to correctly predict firm's revenue growth rate (RGR), which is an effective evaluation indicator for them to see how big the potential power of future development is and measure how about the growth of future development for a target firm that may be selected to investment portfolios. However, conventional methods of forecasting RGR have some shortcomings such as statistical methods based on strict assumptions of linearity and/or normality limit applications in real world. Additionally, due to rapid changing of information technology (IT) today, some techniques (i.e. rough sets and data mining tools) have become important research trends to both practitioners and academicians. With these reasons above, a new procedure, using the feature selection method and rough sets classifier, is proposed to extract decision rules and improve accuracy rate for classifying RGR. In empirical study, an actual RGR dataset collected from publicly traded company of stock markets is employed to illustrate the proposed procedure. The experimental results of RGR dataset analyses indicate that the proposed procedure surpasses the listing methods in terms of both higher accuracy and fewer attributes, and the output of proposed procedure is to generate a set of easily understandable decision rules that are readily applied in knowledge-based investment systems by investors.