Parallel Feature Selection inspired by Group Testing
This paper presents a parallel feature selection method for classification that scales up to very high dimensions and large data sizes. Our original method is inspired by group testing theory, under which the feature selection procedure consists of a collection of randomized tests to be performed in parallel. Each test corresponds to a subset of features, for which a scoring function may be applied to measure the relevance of the features in a classification task. We develop a general theory providing sufficient conditions under which true features are guaranteed to be correctly identified. Superior performance of our method is demonstrated on a challenging relation extraction task from a very large data set that have both redundant features and sample size in the order of millions. We present comprehensive comparisons with state-of-the-art feature selection methods on a range of data sets, for which our method exhibits competitive performance in terms of running time and accuracy. Moreover, it also yields substantial speedup when used as a pre-processing step for most other existing methods.
This paper proposes a novel method for feature selection based on independent group testing, which is desirable for parallel execution for feature selection. The method consists of three steps: (1) test design, (2) score function, and (3) feature identification. In test design, groups of features for being tested are determined, score function assigns a score to each group, and feature identification step finally selects a set of features based on the scores. This paper presented a theory including the number of tests needed to maintain feature selection accuracy, and the experimental results which support that the proposed approach outperforms the competitors in terms of accuracy, scalability and robustness. Major comments:1. In line 132, groups were randomly generated. Are there advantages or disadvantages of making small or large groups?In test design, it would be useful if authors include more discussion on how groups of features should be made both in theory and practice.2. In experiments, as competing methods, popular sparse regression based feature selection methods should be included (e.g. Lasso or L1-logistic regression).3. In Figure 2, why does the proposed method improve the accuracy of FGM but not MIM when used as preprocessing step?4. It would be good to add more synthetic experiments to verify that theory is in agreement with the experiments.5. Conclusion or discussion section is missing. Minor comments:1. In line 82, there a typo. schem -> scheme 2. Section 2 and 3 can be structured for better readability. 3. In Figure 1, x-axis label is missing. 4. In Figure 2, two dotted lines overlap, and it is hard to distinguish between the two lines.