A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Machine Learning
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A feature selection technique for classificatory analysis
Pattern Recognition Letters
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Evaluation of ordinal attributes at value level
Data Mining and Knowledge Discovery
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Attribute reduction based on evidence theory in incomplete decision systems
Information Sciences: an International Journal
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Electronic promotion to new customers using mkNN learning
Information Sciences: an International Journal
Getting insights from the voices of customers: Conversation mining at a contact center
Information Sciences: an International Journal
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Concept-based learning of human behavior for customer relationship management
Information Sciences: an International Journal
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The analysis of customer satisfaction datasets has shown that product-related features fall into three categories (i.e., basic, performance, and excitement), which affect overall satisfaction differently. Because the relationship between product features and customer satisfaction is characterized by non-linearity and asymmetry, feature values are studied to understand the characteristics of a feature. However, existing methods are computationally expensive and work for ordinal features only. We propose a rule-based method that can be used to analyze data features regarding various characteristics of customer satisfaction. The inputs for these rules are derived by using a probabilistic feature-selection technique. In this feature selection method, mutual associations between feature values and class decisions in a pre-classified database are computed to measure the significance of feature values. The proposed method can be used for both types of features: ordinal and categorical. The proposed method is more computationally efficient than previously recommended methods. We performed experiments on a synthetic dataset with known characteristics, and our method correctly predicted the characteristics of the dataset. We also performed experiments with a real-housing dataset. The knowledge extracted from the dataset by using this method is in agreement with the domain knowledge.