Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
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Machine learning is a powerful paradigm within which to analyze 1H-MRS spectral data for the classification of tumour pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this study we apply several feature selection algorithms in order to reduce the complexity of the problem on two types of 1H-MRS spectral data: long-echo and short-echo time, which present considerable differences in the spectrum for the same cases. The obtained experimental results show that the feature selection algorithms enhance the classification performance of the final induced models both in terms of prediction accuracy and number of involved spectral frequencies. The results obtained using a fast algorithm based on entropic measures of subsets of spectral data are specially promising.