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
A methodology to explain neural network classification
Neural Networks
Evaluating Training Data Suitability for Decision Tree Induction
Journal of Medical Systems
Web page feature selection and classification using neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Information Sciences: an International Journal - Special issue: Soft computing data mining
Input Variable Selection: Mutual Information and Linear Mixing Measures
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Data Mining
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
A scalable supervised algorithm for dimensionality reduction on streaming data
Information Sciences: an International Journal
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Machine learning method for knowledge discovery experimented with otoneurological data
Computer Methods and Programs in Biomedicine
A non-symbolic implementation of abdominal pain estimation in childhood
Information Sciences: an International Journal
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
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We improved the classification ability of multilayer perceptron networks by constructing a set of networks of as many as output classes and investigated the influence of different input variables on the classification. We have developed methods named scattering, spectrum and response analysis to express the classification complexity, especially the overlap of output classes, to disentangle the relation between the input variables and output classes of perceptron neural networks, and to establish the importance of input variables. The methods were tested by exploring complicated otoneurological data. In contrast to the variable selection problem, our methods characterize the importance of variables for classification and also describe the importance of the different values of each variable for output (disease) classes. When complex data is distributed in a biased manner between disease classes, we improved classification accuracy by developing a network set called NetSet, which increased average sensitivity and positive predictive value for at least 10% up to 85% and 83% respectively, compared to our earlier neural network classifications with the same data, which clarified class distribution effects and supported our comprehension of the significance of input.