Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Data preparation for data mining
Data preparation for data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Principles of data mining
Evaluating Training Data Suitability for Decision Tree Induction
Journal of Medical Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Ranking a random feature for variable and feature selection
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Variable Selection: Mutual Information and Linear Mixing Measures
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Information Sciences: an International Journal
Computer Methods and Programs in Biomedicine
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
Hybrid sampling for imbalanced data
Integrated Computer-Aided Engineering - Selected papers from the IEEE Conference on Information Reuse and Integration (IRI), July 13-15, 2008
Integrated Computer-Aided Engineering
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
COG: local decomposition for rare class analysis
Data Mining and Knowledge Discovery
A co-classification approach to learning from multilingual corpora
Machine Learning
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Identification of anatomic retinal structures for macular delineation in fluorescein angiograms
Integrated Computer-Aided Engineering
Sharing hardware resources in heterogeneous computer-supported collaboration scenarios
Integrated Computer-Aided Engineering
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We designed an algorithm in order to examine the importance of variables in data sets for variable evaluation and weighting. In particular, it is designated for the evaluation whether a data set includes such information that is useful for the separation of classes in classification and prediction. Such an evaluation can be performed for an entire data set or separately classes or variables. The scatter method is based on traversing through a data set as near neighbour cases and counting class changes, i.e., when the classes of near cases are changed. The fewer the changes, the more compact the classes are in a variable space so that they are possible to separate with high classification accuracy. We tested the method with different data sets of medical origin. Their results showed that the scatter method can be used to explore how separable the classes in these data sets were. This is useful for variable evaluation and weighting.