Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: G-SMOTE: A GMM-based synthetic minority oversampling technique for
  imbalanced learning   Imbalanced Learning is an important learning algorithm for the classification
models, which have enjoyed much popularity on many applications. Typically,
imbalanced learning algorithms can be partitioned into two types, i.e., data
level approaches and algorithm level approaches. In this paper, the focus is to
develop a robust synthetic minority oversampling technique which falls the
umbrella of data level approaches. On one hand, we proposed a method to
generate synthetic samples in a high dimensional feature space, instead of a
linear sampling space. On the other hand, in the proposed imbalanced learning
framework, Gaussian Mixture Model is employed to distinguish the outliers from
minority class instances and filter out the synthetic majority class instances.
Last and more importantly, an adaptive optimization method is proposed to
optimize these parameters in sampling process. By doing so, an effectiveness
and efficiency imbalanced learning framework is developed.

26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
2
The categories of this paper are: math
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex