Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

This article presents a general sampling method, called granular-ball sampling (GBS), for
classification problems by introducing the idea of granular computing. The GBS method …

Partial label learning refers to the issue that each training sample corresponds to a
candidate label set containing only one valid label. Feature selection can be viewed as an …

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 …

Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because
it requires the distance matrix to compute the density and-distance for each object, which …

This paper presents a novel accelerated exact-means called as “Ball-means” by using the
ball to describe each cluster, which focus on reducing the point-centroid distance …

Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power
consumption and contributing to several challenges encountered when transiting to an …

Granular-ball computing (GBC) is an efficient, robust, and scalable learning method for
granular computing. The granular ball (GB) generation method is based on GB computing …

Label distribution learning is a widely studied supervised learning diagram that can handle
the problem of label ambiguity. The increasing size of datasets is accompanied by the …

This paper presents the first review of noise models in classification covering both label and
attribute noise. Their study reveals the lack of a unified nomenclature in this field. In order to …