Abstract Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary
subject, and is referred to as, multi-sensor information fusion which was originated in the …

Supervised feature selection research has a long history. Its popularity exploded in the past
30 years due to the advance of information technology and the need to analyze high …

High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Feature selection is a popular technique of preprocessing data. In order to deal with
dynamic or large data, incremental feature selection has been developed, in which the …

Social progress normally occurs through a sequence of struggles and conflicts, and there
has been relatively little progress in developing effective methods for conflict analysis …

Data Mining is one of the most challenging tasks in a dynamic environment due to rapid
growth of data with respect to time. Dimension reduction, the key process of relevant feature …

In multi-label learning, since different labels may have some distinct characteristics of their
own, multi-label learning approach with label-specific features named LIFT has been …

Feature selection with rough sets aims to delete redundant conditional features from static
data by considering single type features. However, traditional feature selection methods …

Feature selection based on fuzzy rough sets is an effective approach to select a compact
feature subset that optimally predicts a given decision label. Despite being studied …

Feature selection effectively reduces the dimensionality of data. For feature selection, rough
set theory offers a systematic theoretical framework based on consistency measures, of …