Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

The approximation of low-order information-theoretic terms for feature selection approaches
has achieved success in addressing high-dimensional multi-label data. However, three …

Feature selection problems have been extensively studied in the setting of linear estimation
(eg LASSO), but less emphasis has been placed on feature selection for non-linear …

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different
subset of features about largely overlapping sets of data sample (s), to jointly train a useful …

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health
management (PHM) has led to a performance increase in diagnostics, prognostics, and …

Abstract In recent years, Deep Learning models have shown a great performance in
complex optimization problems. They generally require large training datasets, which is a …

Abstract Background Convolutional Neural Networks can be effectively used only when data
are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of …

With the popularity and development of Internet of Things (IoT) technology, human activity
recognition using IoT devices such as wearable sensors can be implemented for various …

As an effective data preprocessing step, feature selection has shown its effectiveness to
prepare high-dimensional data for many machine learning tasks. The proliferation of high di …

Feature selection is an important data preprocessing strategy, has been proven empirically
that it contributes to reducing the dimensionality of feature and enhancing the performance …