  Classifying transients based on multi band light curves is a challenging but
crucial problem in the era of GAIA and LSST since the sheer volume of
transients will make spectroscopic classification unfeasible. Here we present a
nonparametric classifier that uses the transient's light curve measurements to
predict its class given training data. It implements two novel components: the
first is the use of the BAGIDIS wavelet methodology - a characterization of
functional data using hierarchical wavelet coefficients. The second novelty is
the introduction of a ranked probability classifier on the wavelet coefficients
that handles both the heteroscedasticity of the data in addition to the
potential non-representativity of the training set. The ranked classifier is
simple and quick to implement while a major advantage of the BAGIDIS wavelets
is that they are translation invariant, hence they do not need the light curves
to be aligned to extract features. Further, BAGIDIS is nonparametric so it can
be used for blind searches for new objects. We demonstrate the effectiveness of
our ranked wavelet classifier against the well-tested Supernova Photometric
Classification Challenge dataset in which the challenge is to correctly
classify light curves as Type Ia or non-Ia supernovae. We train our ranked
probability classifier on the spectroscopically-confirmed subsample (which is
not representative) and show that it gives good results for all supernova with
observed light curve timespans greater than 100 days (roughly 55% of the
dataset). For such data, we obtain a Ia efficiency of 80.5% and a purity of
82.4% yielding a highly competitive score of 0.49 whilst implementing a truly
"model-blind" approach to supernova classification. Consequently this approach
may be particularly suitable for the classification of astronomical transients
in the era of large synoptic sky surveys.
