Abstract
Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM's algorithms using real high-dimensional data from various applications.
Highlights
Given a target variable Y of n measurements and a set X of p features the problem of feature selection (FS) is to identify the minimal set of features with the highest predictabilitya on the target variable of interest
The natural question that arises, is why should researchers and practitioners perform FS. The answer to this is for a variety of reasons[1], such as: a) many features may be expensive to measure, especially in the clinical and medical domains; b) FS may result in more accurate models by removing noise while treating the curse-of-dimensionality; c) the final produced parsimonious models are computationally cheaper and often easier to understand and interpret; d) future experiments can benefit from prior feature selection tasks and provide more insight into the problem of interest, its characteristics and structure. e) FS is indissolubly connected with causal inference that tries to identify the system’s causal mechanism that generated the data
2 (1.08%) R packages treat the case of FS with multiple datasetsh while only 4 (2.17%) packages are devised for high volume data
Summary
Any reports and responses or comments on the article can be found at the end of the article. This article is included in the RPackage gateway. We are grateful to the reviewers for their time to read the paper and the comments they raised. We have addressed all comments raised by the reviewers and proof read it and made some additional changes. We hope the paper is easier to read . Any further responses from the reviewers can be found at the end of the article
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