Mutual information based feature subset selection in multivariate time series classification

This paper deals with supervised classification of multivariate time se- ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown et al. [10]. The key point in this framework is the computation of the mutual information between the features, which allows us to measure the relevance of each feature subset. In our case, where the features are a time series, we use an adaptation of existing nonparametric mutual infor- mation estimators based on the k-nearest neighbor. Specifically, for the purpose of bringing these methods to the time series scenario, we rely on the use of dynamic time warping dissimilarity. Our experimental results show that our method is able to strongly reduce the number of time series while keeping or increasing the classification accuracy.Grant agreement no. KK-2019/00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

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