Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
An introduction to variable and feature selection
The Journal of Machine Learning Research
Identification of attribute interactions and generation of globally relevant continuous features in machine learning
Estimation of the information by an adaptive partitioning of the observation space
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
EURASIP Journal on Advances in Signal Processing
Feature selection based on run covering
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
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Determining the most appropriate inputs to a model has a significant impact on the performance of the model and associated algorithms for classification, prediction, and data analysis. Previously, we proposed an algorithm ICAIVS which utilizes independent component analysis (ICA) as a preprocessing stage to overcome issues of dependencies between inputs, before the data being passed through to an inout variable selection (IVS) stage. While we demonstrated previously with artificial data that ICA can prevent an overestimation of necessary input variables, we show here that mixing between input variables is common in real-world data sets so that ICA preprocessing is useful in practice. This experimental test is based on new measures introduced in this paper. Furthermore, we extend the implementation of our variable selection scheme to a statistical dependency test based on mutual information and test several algorithms on Gaussian and sub-Gaussian signals. Specifically, we propose a novel method of quantifying linear dependencies using ICA estimates of mixing matrices with a new Linear Mixing Measure (LMM).