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
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Integrating feature and instance selection for text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Consistency-based search in feature selection
Artificial Intelligence
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
Distance based feature selection for clustering microarray data
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
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In this paper we propose TwoWayFocused classification that performs feature selection and tuple selection over the data before performing classification. Although feature selection and tuple selection have been studied earlier in various research areas such as machine learning, data mining, and so on, they have rarely been studied together. The contribution of this paper is that we propose a novel distance measure to select the most representative features and tuples. Our experiments are conducted over some microarray gene expression datasets, UCI machine learning and KDD datasets. Results show that the proposed method outperforms the existing methods quite significantly.