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
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Nonlinear and Noncompensatory Models in User Information Satisfaction Measurement
Information Systems Research
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Ordinal Evaluation: A New Perspective on Country Images
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
A rule-based method for identifying the factor structure in customer satisfaction
Information Sciences: an International Journal
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We propose a novel context sensitive algorithm for evaluation of ordinal attributes which exploits the information hidden in ordering of attributes' and class' values and provides a separate score for each value of the attribute. Similar to feature selection algorithm ReliefF, the proposed algorithm exploits the contextual information via selection of nearest instances. The ordEval algorithm outputs probabilistic factors corresponding to the effect an increase/decrease of attribute's value has on the class value. While the ordEval algorithm is general and can be used for analysis of any survey with graded answers, we show its utility on an important marketing problem of customer (dis)satisfaction. We develop a visualization technique and show how we can use it to detect and confirm several findings from marketing theory.