17 research outputs found
Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data
The main theme of this dissertation focuses on multivariate correlation analysis on different data types and we identify and define various research gaps in the same. For the defined research gaps we develop novel techniques that address relevance of features to the target and redundancy of features amidst themselves. Our techniques aim at handling homogeneous data, i.e., only continuous or categorical features, mixed data, i.e., continuous and categorical features, and time serie
Building robust prediction models for defective sensor data using Artificial Neural Networks
Predicting the health of components in complex dynamic systems such as an
automobile poses numerous challenges. The primary aim of such predictive
systems is to use the high-dimensional data acquired from different sensors and
predict the state-of-health of a particular component, e.g., brake pad. The
classical approach involves selecting a smaller set of relevant sensor signals
using feature selection and using them to train a machine learning algorithm.
However, this fails to address two prominent problems: (1) sensors are
susceptible to failure when exposed to extreme conditions over a long periods
of time; (2) sensors are electrical devices that can be affected by noise or
electrical interference. Using the failed and noisy sensor signals as inputs
largely reduce the prediction accuracy. To tackle this problem, it is
advantageous to use the information from all sensor signals, so that the
failure of one sensor can be compensated by another. In this work, we propose
an Artificial Neural Network (ANN) based framework to exploit the information
from a large number of signals. Secondly, our framework introduces a data
augmentation approach to perform accurate predictions in spite of noisy
signals. The plausibility of our framework is validated on real life industrial
application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has
obtained funding from the Electronic Components and Systems for European
Leadership (ECSEL) Joint Undertaking, the framework programme for research
and innovation Horizon 2020 (2014-2020) under grant agreement number
662189-MANTIS-2014-
Data_AppliedDataScience
Data for MANTIS-Bosch collaboration paper submitted for ECML-PKDD 2018.<div><br><div><div><br></div></div></div