Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature learning in feature-sample networks using multi-objective
  optimization   Data and knowledge representation are fundamental concepts in machine
learning. The quality of the representation impacts the performance of the
learning model directly. Feature learning transforms or enhances raw data to
structures that are effectively exploited by those models. In recent years,
several works have been using complex networks for data representation and
analysis. However, no feature learning method has been proposed for such
category of techniques. Here, we present an unsupervised feature learning
mechanism that works on datasets with binary features. First, the dataset is
mapped into a feature--sample network. Then, a multi-objective optimization
process selects a set of new vertices to produce an enhanced version of the
network. The new features depend on a nonlinear function of a combination of
preexisting features. Effectively, the process projects the input data into a
higher-dimensional space. To solve the optimization problem, we design two
metaheuristics based on the lexicographic genetic algorithm and the improved
strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced
network contains more information and can be exploited to improve the
performance of machine learning methods. The advantages and disadvantages of
each optimization strategy are discussed.

19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an