Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature selection algorithm based on Catastrophe model to improve the
  performance of regression analysis   In this paper we introduce a new feature selection algorithm to remove the
irrelevant or redundant features in the data sets. In this algorithm the
importance of a feature is based on its fitting to the Catastrophe model.
Akaike information crite- rion value is used for ranking the features in the
data set. The proposed algorithm is compared with well-known RELIEF feature
selection algorithm. Breast Cancer, Parkinson Telemonitoring data and Slice
locality data sets are used to evaluate the model.

37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg