The concept of dependency in a neighborhood rough set model is an important evaluation
function for the feature selection. This function considers only the classification information …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …

Feature selection aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

Neighborhood rough sets are widely used as an effective tool to deal with numerical data.
However, most of the existing neighborhood granulation models cannot well describe the …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …

摘要在大数据时代, 如何充分挖掘出蕴藏于数据资源中的价值正在成为各国IT 业界,
学术界和政府共同关注的焦点. 使用云计算平台分布式地存储和分析大数据已经成为共识并且 …

多粒度数据是一种特殊的, 有用的数据类型, 它通过对论域(研究对象的集合)
采用不同的粒化方式使得数据能够在多个粒度空间中进行呈现, 在此基础上可以开展数据的多 …

In multi-label learning, label correlations commonly exist in the data. Such correlation not
only provides useful information, but also imposes significant challenges for multi-label …

In recent years, granular computing has been developed as a unified data description
paradigm. As a popular soft computing supervised learning model, rough sets theory-based …

Exploring rough sets from the perspective of multigranulation represents a promising
direction in rough set theory, where concepts are approximated by multiple granular …