  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.
