Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature Selection for Regression Problems Based on the Morisita
  Estimator of Intrinsic Dimension   Data acquisition, storage and management have been improved, while the key
factors of many phenomena are not well known. Consequently, irrelevant and
redundant features artificially increase the size of datasets, which
complicates learning tasks, such as regression. To address this problem,
feature selection methods have been proposed. This paper introduces a new
supervised filter based on the Morisita estimator of intrinsic dimension. It
can identify relevant features and distinguish between redundant and irrelevant
information. Besides, it offers a clear graphical representation of the
results, and it can be easily implemented in different programming languages.
Comprehensive numerical experiments are conducted using simulated datasets
characterized by different levels of complexity, sample size and noise. The
suggested algorithm is also successfully tested on a selection of real world
applications and compared with RReliefF using extreme learning machine. In
addition, a new measure of feature relevance is presented and discussed.

8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int