… feature selection, we describe two approaches: (i) homogeneous, ie, using the same feature
selection … and (ii) heterogeneous, ie, using different feature selection methods with the same …

… feature models, selection techniques and classifiers. The main contribution of this paper is
the simultaneous use of distinct types of features and the posterior phase of feature selection. …

… , which has been successfully applied to heterogeneous feature selection. In this article, the
… design a feature selection method to handle categorical, numerical, and heterogeneous data…

… heterogeneous feature selection methods are only used for static ordered data. For dynamic
ordered data, existing heterogeneous feature selection … heterogeneous feature selection …

… framework for grouped feature selection with multi-modal … heterogeneous feature selection
framework, we review some works related to multiple feature integration and feature selection …

… feature selection methods based on FRSs. Based on the above analyses, this article proposes
an unsupervised heterogeneous feature selection … each feature, and thus, the feature with …

… feature selection repository that consists of most of the popular feature selection algorithms
(http://featureselection.… we review current feature selection algorithms for heterogeneous data …

… suggest applying a random feature selection technique to increase the diverse of selected
features. Compared with the standard exhaustive Adaboost algorithm for feature selection, the …

… Heterogeneous feature selection will be detailed in this section. (2) Model construction:
First, determine the combination of baseline model and meta-classifier through repeated …

… of feature selection algorithms with the similarity of feature rankings and that of feature …
k subsets and use k-1 subsets to rank features using feature selection algorithms. We get k …