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Abstract-Feature subset selection (FSS) is a known technique to preprocess the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated the data. We propose a family of novel unsupervised methods for feature subset selection from Multivariate Time Series (MTS) based on Common Principal Component Analysis, termed C CLeV V er. Traditional FSS techniques, such as Recursive Feature Elimination (RFE) and Fisher Criterion (FC), have been applied to MTS data sets, e.g., Brain Computer Interface (BCI) data sets. However, these techniques may lose the correlation information among features, while our proposed techniques utilize the properties of the principal component analysis to retain that information. In order to evaluate the effectiveness of our selected subset of features, we employ classification as the target data mining task. Our exhaustive experiments show that C CLeV V er outperforms RFE, FC, and random selection by up to a factor of two in terms of the classification accuracy, while taking up to 2 orders of magnitude less processing time than RFE and FC.

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This paper describes an approach towards automating the identification of design problems with three-dimensional mediated or gaming environments through the capture and query of user-player behavior represented as a data schema that we have termed "immersidata". Analysis of data from a study of an educational computer game that we are developing shows that this approach is an effective way to pinpoint potential usability or design problems occurring in unfolding situational and episodic events that can interrupt or break user experience. As well as informing redesign, a key advantage of this cost-effective approach is that it considerably reduces the time evaluators spend analyzing hours of videoed study material. Categories INTRODUCTIONThe non-linear, continuous and real-time interactive nature of three-dimensional mediated or computer/digital gaming environments presents novel challenges to humancomputer interaction. As academia and research enthusiastically adopt and develop computer games for areas such as education, training and entertainment, the need for more considered design and evaluation methods becomes apparent.