A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
On learning to predict web traffic
Decision Support Systems - Special issue: Web data mining
An empirical evaluation of classifier combination schemes for predicting user navigational behavior
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing
IEEE Transactions on Knowledge and Data Engineering
Applying incremental tree induction to retrieval from manuals and medical texts
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
Parallelizing Feature Selection
Algorithmica
Automatic texture feature selection for image pixel classification
Pattern Recognition
The effects of metaphors on novice and expert learners' performance and mental-model development
Interacting with Computers
An intelligent learning diagnosis system for Web-based thematic learning platform
Computers & Education
Constrained Cascade Generalization of Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Application of neural networks and Kano's method to content recommendation in web personalization
Expert Systems with Applications: An International Journal
Ontology-based data mining approach implemented on exploring product and brand spectrum
Expert Systems with Applications: An International Journal
Information problem solving by experts and novices: analysis of a complex cognitive skill
Computers in Human Behavior
Navigation in hypermedia learning systems: experts vs. novices
Computers in Human Behavior
Expert system based on artificial neural networks for content-based image retrieval
Expert Systems with Applications: An International Journal
Learning a model of a web user's interests
UM'03 Proceedings of the 9th international conference on User modeling
An evaluation of filter and wrapper methods for feature selection in categorical clustering
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
A multiple classifier system for early melanoma diagnosis
Artificial Intelligence in Medicine
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Hi-index | 12.05 |
Wrapper feature selection approaches are widely used to choose a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier. The downside to this is that each classifier will have its own biases and will therefore select very different features. To overcome the biases of individual classifiers, we propose a new data mining method called Wrapper-based Decision Trees (WDT). The WDT method uses multiple classifiers for selecting relevant features and decision trees to visualize relationships among the selected features. We use the WDT to investigate the influences of the levels of computer experience on users' preferences for the design of search engines. The benefit of using WDT lies within the fact that it can uncover the most accurate set of relevant features to help differentiate the preferences of users with diverse levels of computer experience. The results indicate that the users with varied levels of computer experiences have different preferences regarding the following features: the number of icons, the arrangement of search results, and the presentation of error messages. Such findings can be used to develop personalized search engines to accommodate users' different levels of computer experience.