Guiding feature subset selection with an interactive visualization

Abstract

We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…The authors later proposed a technique called SmartStripes [65] where they investigate the relations between different subsets of features and entities. Interactive systems have also been used to help create decision trees [98] (see Figure 4).…”

“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

Putting the Scientist in the Loop -- Accelerating Scientific Progress with Interactive Machine Learning

“…Presently this is a one time decision based on intuition. Interfaces could be designed that allow the user to interactively explore the contribution of different features [73].…”

Dis-function: Learning distance functions interactively

“…In contrast, our method as described in Section 4 provides several coordinated views of the data and does not restrict the user to formulate only pairwise constraints. May, et al [27] present the SmartStripes system which assists the user in feature subset selection by visualizing the dependencies and interdependencies between different features and entity subsets. This work is similar to ours in that both methods seek to identify relevant dimensions in a highdimensional dataset.…”