78 citations · 97 across the 12 of their papers we have counts for
15 papers
Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery
Chuyao Zhang, E Li, Taochen Chen +5
Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis. Real-world datasets often exhibit complex…
Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering
Mingjie Zhao, Yunfan Zhang, Yiqun Zhang +1
Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing methods rely on data-level s…
Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning
Shenghong Cai, Yiqun Zhang, Xiaopeng Luo +3
Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, th…
Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
Wenbin Pei, Ruohao Dai, Bing Xue +3
Time series classification is a fundamental machine learning task with broad real-world applications. Although many deep learning methods have proven effective in learning time-ser…
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
Zihua Yang, Xin Liao, Yiqun Zhang +1
Qualitative data are widespread in domains such as healthcare, marketing, and bioinformatics, where clustering offers a fundamental tool for pattern discovery. A core difficulty of…
TYrPPG: Uncomplicated and Enhanced Learning Capability rPPG for Remote Heart Rate Estimation
Taixi Chen, Yiu-ming Cheung
Remote photoplethysmography (rPPG) can remotely extract physiological signals from RGB video, which has many advantages in detecting heart rate, such as low cost and no invasion to…
CADM: Cluster-customized Adaptive Distance Metric for Categorical Data Clustering
Taixi Chen, Yiu-ming Cheung, Yiqun Zhang
An appropriate distance metric is crucial for categorical data clustering, as the distance between categorical data cannot be directly calculated. However, the distances between at…
MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction
Zexi Tan, Tao Xie, Binbin Sun +3
Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs). Early and accurate sepsis prediction (SP) is critical for timely int…
GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
Jintao Huang, Yiu-ming Cheung, Chi-man Vong +1
Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo…
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning
Wenbin Pei, Ruohao Dai, Bing Xue +4
Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class…
Categorical Data Clustering via Value Order Estimated Distance Metric Learning
Yiqun Zhang, Mingjie Zhao, Hong Jia +3
Clustering is a popular machine learning technique for data mining that can process and analyze datasets to automatically reveal sample distribution patterns. Since the ubiquitous…
Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering
Yiqun Zhang, Sen Feng, Pengkai Wang +5
Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encoun…
PLOOD: Partial Label Learning with Out-of-distribution Objects
Jintao Huang, Yiu-Ming Cheung, Chi-Man Vong
Existing Partial Label Learning (PLL) methods posit that training and test data adhere to the same distribution, a premise that frequently does not hold in practical application wh…
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
Xiaoge Zhang, Xiao-Lin Wang, Fenglei Fan +2
In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive perform…
Competitive and Penalized Clustering Auto-encoder
Zihao Wang, Yiuming Cheung
The paper has been withdrawn since more effective experiments should be completed. Auto-encoders (AE) has been widely applied in different fields of machine learning. However, as a…