C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
A new version of the rule induction system LERS
Fundamenta Informaticae
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
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
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Creating Effective Load Models for Performance Testing with Incomplete Empirical Data
WSE '04 Proceedings of the Web Site Evolution, Sixth IEEE International Workshop
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extracting classification rule of software diagnosis using modified MEPA
Expert Systems with Applications: An International Journal
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Automatic extraction and identification of chart patterns towards financial forecast
Applied Soft Computing
Soft computing system for bank performance prediction
Applied Soft Computing
Attribute selection based on a new conditional entropy for incomplete decision systems
Knowledge-Based Systems
Decision rule mining using classification consistency rate
Knowledge-Based Systems
Entropy measures and granularity measures for set-valued information systems
Information Sciences: an International Journal
Rough set approach to incomplete numerical data
Information Sciences: an International Journal
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In the financial markets, due to limitations of the noise caused continuously by changing market conditions and environments, and a subjective sentiment or other factors unrelated to expected returns on investment decision-making of investors, there is a growing consensus designing and employing a variety of soft computing systems to remedy the aforementioned existing problems objectively and intelligently. Previously, many researchers have long used statistical methods for handling the related problems of investment markets. However, these conventional methods become more complex when relationships in the input/output dataset are nonlinear. Nevertheless, statistical techniques always rely on the assumptions on linear separability, multivariate normality, and independence of the predictive variables; unfortunately, many of the common models of treating the financial markets problems violate these assumptions. Therefore, to reconcile the existing shortcomings, this study offers three hybrid models based on a rough sets classifier to extract decision rules and aid making investment decision for the market investors. The proposed hybrid models include three differently integrated models for solving IPO (Initial Public Offerings) returns problems of the financial markets: (1) Experiential Knowledge (EK)+Feature Selection Method (FSM)+Minimize Entropy Principle Approach (MEPA)+Rough Set Theory (RST)+Rule Filter (RF), (2) EK+Decision Trees (DT)-C4.5+RST+RF, and (3) EK+FSM+RST+RF. The proposed hybrid models are illustrated by examining an IPO dataset for publicly traded firms. The experimental results indicate that the proposed hybrid models outperform the listing methods in accuracy, number of attributes, standard deviation, and number of rules. Furthermore, the proposed hybrid models generate comprehensible rules readily applied in knowledge-based systems for investors. Meaningfully, the study findings and implications are of value to both academicians and practitioners.