Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Adaptive Minority Oversampling Technique for Improved
  Classification in Data Imbalanced Scenarios   Imbalance in the proportion of training samples belonging to different
classes often poses performance degradation of conventional classifiers. This
is primarily due to the tendency of the classifier to be biased towards the
majority classes in the imbalanced dataset. In this paper, we propose a novel
three step technique to address imbalanced data. As a first step we
significantly oversample the minority class distribution by employing the
traditional Synthetic Minority OverSampling Technique (SMOTE) algorithm using
the neighborhood of the minority class samples and in the next step we
partition the generated samples using a Gaussian-Mixture Model based clustering
algorithm. In the final step synthetic data samples are chosen based on the
weight associated with the cluster, the weight itself being determined by the
distribution of the majority class samples. Extensive experiments on several
standard datasets from diverse domains shows the usefulness of the proposed
technique in comparison with the original SMOTE and its state-of-the-art
variants algorithms.

26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom