The main goal of this study was to use the synthetic minority oversampling technique
(SMOTE) to expand the quantity of landslide samples for machine learning methods (ie …

Online streaming feature selection which deals with streaming features in an online manner
plays a critical role in big data problems. Many approaches have been proposed to handle …

In this study, a granular ball based selector was developed for reducing the dimensions of
data from the perspective of attribute reduction. The granular ball theory offers a data …

A lot of candidate features are usually provided to a learning algorithm for producing a
complete characterization of the classification task. However, it is often the case that majority …

In terms of neighborhood rough sets, the tri-level granular structure of neighborhood system
(carrying the neighborhood granule, swarm, and library) establishes a granular computing …

Intelligent forecasting model of stock price is an effective way to obtain ideal investment
returns. Due to the impact of quantitative transactions, traditional forecasting methods face …

Set-valued information systems are generalized models of single-valued information
systems. The attribute set in the set-valued information system may evolve over time when …

The k-nearest-neighbor rule is a popular classification technique, and rough set theory is an
effective mathematical tool to deal with the uncertainty of data. Rough set models based on k …

This paper proposes a Cascaded Random Forest (CRF) method, which can improve the
classification performance by means of combining two different enhancements into the …

Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …