Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aggregation using input-output trade-off   In this paper, we introduce a new learning strategy based on a seminal idea
of Mojirsheibani (1999, 2000, 2002a, 2002b), who proposed a smart method for
combining several classifiers, relying on a consensus notion. In many
aggregation methods, the prediction for a new observation x is computed by
building a linear or convex combination over a collection of basic estimators
r1(x),. .. , rm(x) previously calibrated using a training data set.
Mojirsheibani proposes to compute the prediction associated to a new
observation by combining selected outputs of the training examples. The output
of a training example is selected if some kind of consensus is observed: the
predictions computed for the training example with the different machines have
to be "similar" to the prediction for the new observation. This approach has
been recently extended to the context of regression in Biau et al. (2016). In
the original scheme, the agreement condition is actually required to hold for
all individual estimators, which appears inadequate if there is one bad initial
estimator. In practice, a few disagreements are allowed ; for establishing the
theoretical results, the proportion of estimators satisfying the condition is
required to tend to 1. In this paper, we propose an alternative procedure,
mixing the previous consensus ideas on the predictions with the Euclidean
distance computed between entries. This may be seen as an alternative approach
allowing to reduce the effect of a possibly bad estimator in the initial list,
using a constraint on the inputs. We prove the consistency of our strategy in
classification and in regression. We also provide some numerical experiments on
simulated and real data to illustrate the benefits of this new aggregation
method. On the whole, our practical study shows that our method may perform
much better than the original combination technique, and, in particular,
exhibit far less variance. We also show on simulated examples that this
procedure mixing inputs and outputs is still robust to high dimensional inputs.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ