We describe a methodology to classify periodic variable stars identified
using photometric time-series measurements constructed from the Wide-field
Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases.
This will assist in the future construction of a WISE Variable Source Database
that assigns variables to specific science classes as constrained by the WISE
observing cadence with statistically meaningful classification probabilities.
We have analyzed the WISE light curves of 8273 variable stars identified in
previous optical variability surveys (MACHO, GCVS, and ASAS) and show that
Fourier decomposition techniques can be extended into the mid-IR to assist with
their classification. Combined with other periodic light-curve features, this
sample is then used to train a machine-learned classifier based on the random
forest (RF) method. Consistent with previous classification studies of variable
stars in general, the RF machine-learned classifier is superior to other
methods in terms of accuracy, robustness against outliers, and relative
immunity to features that carry little or redundant class information. For the
three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae
Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%,
and 84.5% respectively using cross-validation analyses, with 95% confidence
intervals of approximately +/-2%. These accuracies are achieved at purity (or
reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that
achieved in previous automated classification studies of periodic variable
stars.Comment: 48 pages, 17 figures, 1 table, accepted by A
