Three-way improved neighborhood entropies based on three-level granular structures

Zhang, Xianyong; Zhou, Yanhong; Tang, Xiao; Fan, Yunrui

doi:10.1007/s13042-021-01493-4

Cited by 10 publications

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Huang et al [29] combined the distance function with the three-way decision to compute the neighborhood for mixed data. Zhang et al [30] applied the three-way decision to the concept of the neighborhood information system and decomposed it into a three-level structure. Kong et al [31] divided all attributes in the information table into three disjoint sets and developed a new granular structure for granular computing.…”

Section: Introductionmentioning

confidence: 99%

Three-Way Co-Training with Pseudo Labels for Semi-Supervised Learning

Wang,

Gao,

Zhou

et al. 2023

Mathematics

View full text Add to dashboard Cite

The theory of three-way decision has been widely utilized across various disciplines and fields as an efficient method for both knowledge reasoning and decision making. However, the application of the three-way decision theory to partially labeled data has received relatively less attention. In this study, we propose a semi-supervised co-training model based on the three-way decision and pseudo labels. We first present a simple yet effective method for producing two views by assigning pseudo labels to unlabeled data, based on which a heuristic attribute reduction algorithm is developed. The three-way decision is then combined with the concept of entropy to form co-decision rules for classifying unlabeled data into useful, uncertain, or useless samples. Finally, some useful samples are iteratively selected to improve the performance of the co-decision model. The experimental results on UCI datasets demonstrate that the proposed model outperforms other semi-supervised models, exhibiting its potential for partially labeled data.

show abstract

Section: Introductionmentioning

confidence: 99%