Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Cascaded Clustering for Classification of Noisy Label
  Data   The performance of supervised classification techniques often deteriorates
when the data has noisy labels. Even the semi-supervised classification
approaches have largely focused only on the problem of handling missing labels.
Most of the approaches addressing the noisy label data rely on deep neural
networks (DNN) that require huge datasets for classification tasks. This poses
a serious challenge especially in process and manufacturing industries, where
the data is limited and labels are noisy. We propose a semi-supervised cascaded
clustering (SSCC) algorithm to extract patterns and generate a cascaded tree of
classes in such datasets. A novel cluster evaluation matrix (CEM) with
configurable hyperparameters is introduced to localize and eliminate the noisy
labels and invoke a pruning criterion on cascaded clustering. The algorithm
reduces the dependency on expensive human expertise for assessing the accuracy
of labels. A classifier generated based on SSCC is found to be accurate and
consistent even when trained on noisy label datasets. It performed better in
comparison with the support vector machines (SVM) when tested on multiple
noisy-label datasets, including an industrial dataset. The proposed approach
can be effectively used for deriving actionable insights in industrial settings
with minimal human expertise.

29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ