47 Result(s)
within
Yi**ang Chen
Artificial Intelligence
Control, Robotics, Mechatronics
Pattern Recognition
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Article
Open AccessDynamic multi-label feature selection algorithm based on label importance and label correlation
Multi-label distribution is a popular direction in current machine learning research and is relevant to many practical problems. In multi-label learning, samples are usually described by high-dimensional featu...
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Undersampling based on generalized learning vector quantization and natural nearest neighbors for imbalanced data
Imbalanced datasets can adversely affect classifier performance. Conventional undersampling approaches may lead to the loss of essential information, while oversampling techniques could introduce noise. To add...
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The concept information of graph granule with application to knowledge graph embedding
Knowledge graph embedding (KGE) has become one of the most effective methods for the numerical representation of entities and their relations in knowledge graphs. Traditional methods primarily utilise triple f...
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An adaptive joint optimization framework for pruning and quantization
Pruning and quantization are among the most widely used techniques for deep learning model compression. Their combined application holds the potential for even greater performance gains. Most existing works co...
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Relabeling and policy distillation of hierarchical reinforcement learning
Hierarchical reinforcement learning (HRL) is a promising method to extend traditional reinforcement learning to solve more complex tasks. HRL can solve the problems of long-term reward sparsity and credit assi...
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Multi-class feature selection via Sparse Softmax with a discriminative regularization
Feature selection plays a critical role in many machine learning applications as it effectively addresses the challenges posed by “the curse of dimensionality” and enhances the generalization capability of tra...
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RSGNN: residual structure graph neural network
Compared to conventional artificial neural networks, Graph Neural Networks (GNNs) better handle graph-structured data. Graph topology plays an important role in learning graph representations and impacts the p...
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Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion
Industrial process data have the characteristics of heterogeneity, dimensional inconsistency and multi time scales, which increase the difficulty of condition evaluation in industrial process using multi-sourc...
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Multiple sparse spaces network pruning via a joint similarity criterion
In this paper, a simple and effective neural network pruning framework is proposed to solve the problems of low model acceleration efficiency and inaccurate identification of pruning channels in conventional m...
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Transformer-based contrastive learning framework for image anomaly detection
Anomaly detection refers to the problem of uncovering patterns in a given data set that do not conform to the expected behavior. Recently, owing to the continuous development of deep representation learning, a...
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Online rule fusion model based on formal concept analysis
A rule is an effective representation of knowledge in formal concept analysis (FCA), which can express the relations between concepts. One of the main research directions of FCA is to develop rule-based classi...
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Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition
With the progressive development of ubiquitous computing, wearable human activity recognition is playing an increasingly important role in many fields, such as health monitoring, disease-assisted diagnostic re...
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Software defect prediction ensemble learning algorithm based on adaptive variable sparrow search algorithm
Software defect prediction has caused widespread concern among software engineering researchers, which aims to erect a software defect prediction model according to historical data. Among all the techniques us...
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A survey of multi-label classification based on supervised and semi-supervised learning
Multi-label classification algorithms based on supervised learning use all the labeled data to train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label all the data n...
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FewJoint: few-shot learning for joint dialogue understanding
Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely relate...
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A novel multiple temporal-spatial convolution network for anode current signals classification
Anode current signals (ACS) play an important role in aluminum reduction production. Owing to the complexity dynamic and temporal-spatial dependency characteristics, classification of ACS is a challenging prob...
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Detection and quantification of anomalies in communication networks based on LSTM-ARIMA combined model
The anomaly detection for communication networks is significant for improve the quality of communication services and network reliability. However, traditional communication monitoring methods lack proactive m...
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Incremental calculation approaches for granular reduct in formal context with attribute updating
Attribute reduction in formal concept analysis is a highly concerned dimensionality reduction method, which purifies formal context by removing unimportant attributes. Current trends of dealing with attribute ...
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Feature fusion network for clothing parsing
Clothing parsing tasks have attracted considerable attention because of their wide application. The challenge of clothing parsing is that clothing images have many characteristics, such as complex textures, di...
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Ranking defects and solving countermeasures for Pythagorean fuzzy sets with hesitant degree
Pythagorean fuzzy set (PFS) is the most concerned and effective tool to describe fuzzy information in the research of machine learning and decision science, its unique representation ability and theoretical me...
