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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liu, S. Dissanayake, S. Patel, S. Dang, X. Mlsna, T. Chen, Y. Wilkins, D. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Comput. & Inf. Sci., Univ. of Mississippi, Oxford, MS, USA (Liu, S.; Chen, Y.; Wilkins, D.) || Dept. of Math., Univ. of Mississippi, Oxford, MS, USA (Dang, X.) || Dept. of Chem., Mississippi State Univ., Starkville, MS, USA (Dissanayake, S.; Mlsna, T.) || Seacoast Sci., Inc., Carlsbad, CA, USA (Patel, S.) |
| Abstract | Regression is widely utilized in a variety of biological problems involving continuous outcomes. There are a number of methods for building regression models ranging from linear models to more complex nonlinear ones. While linear regression techniques can identify linear correlations between input and output, in many practical applications, the relations are nonlinear. These relations can be modeled by nonlinear regression techniques effectively. However, many models built with nonlinear techniques have limited interpretation, which is crucial in many biological problems. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features, and hence is able to provide a simple interpretation. We tested the approach on a seacoast chemical sensors dataset, a Stockori flowering time dataset, and three datasets from the UCI repository. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of conventional random forests regression. It demonstrates high potential in terms of prediction performance and interpretation ease on studying nonlinear relationships of the subjects. |
| Sponsorship | IEEE Comput.Soc. |
| Starting Page | 446 |
| Ending Page | 451 |
| File Size | 240760 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479913091 |
| DOI | 10.1109/BIBM.2013.6732533 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Radio frequency Support vector machines Rule based regression Computed tomography Vegetation Predictive models Feature extraction Prediction algorithms random forests rule extraction stability feature selection |
| Content Type | Text |
| Resource Type | Article |
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