In this paper, heterogeneous ensemble of classifiers is evaluated and the outputs are
integrated by a boosting meta-learner. Both ADABOOST and XGBOOST are tried for the …

Deep neural networks (DNNs) are prone to overfitting and hence have high variance.
Overfitted networks do not perform well for a new data instance. So instead of using a single …

In this study, we introduce an ensemble system by combining homogeneous ensemble and
heterogeneous ensemble into a single framework. Based on the observation that the …

Human Activity Recognition~(HAR) is the classification of human movement, captured using
one or more sensors either as wearables or embedded in the environment~(eg depth …

Bagging and Boosting are two main ensemble approaches consolidating the decisions of
several hypotheses. The diversity of the ensemble members is considered to be a significant …

To find most suitable classifier is possible either through cross-validation, which suffers from
computational cost or through expert advice which is not always feasible to have. Meta …

With the ever-increasing sensor types and complexity in the field of activity recognition,
proper multi-sensor configuration system is essential to balance the recognition …

In heterogeneous ensemble systems, each learning algorithm learns a classifier on a given
training set to describe the relationship between a feature vector and its class label. As each …

Abstract As a discipline, Machine Learning has been adopted and leveraged widely by
researchers from several domains. There is a huge range of classifiers already available in …

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in
many areas of machine learning. Although DNNs have achieved great breakthrough in …