An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Article Automation & Control Systems

Co-training based virtual sample generation for solving the small sample size problem in process industry

Qun-Xiong Zhu et al.

Summary: With the development of industrialization, the production scale and complexity of process industries are increasing. However, it is challenging to establish accurate and efficient data-driven soft sensor models in process industries due to limited samples and uneven sample distribution. This paper proposes a novel virtual sample generation method based on the co-training of two K-Nearest Neighbor (KNN) models to solve this problem. The method includes identifying sparse regions, generating input features in these regions, predicting outputs, screening qualified virtual samples, and updating the model for improved prediction accuracy.

ISA TRANSACTIONS (2023)

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Article Computer Science, Information Systems

An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification

Xiaowei Gu

Summary: A novel semi-supervised EIS is proposed in this paper, which is capable of continuously self-developing and self-updating from unlabelled data streams chunk by chunk through a novel pseudo-labelling strategy, achieving superior classification model performance.

INFORMATION SCIENCES (2022)

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Proceedings Paper Acoustics

LABEL-OCCURRENCE-BALANCED MIXUP FOR LONG-TAILED RECOGNITION

Shaoyu Zhang et al.

Summary: Mixup is a popular data augmentation technique, but may face label suppression issue when applied to long-tailed data. To address this, Label-Occurrence-Balanced Mixup is proposed, using class-balanced samplers to generate new data and improve the adaptability of mixup to imbalanced data.

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (2022)

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Article Computer Science, Artificial Intelligence

An Ensemble Broad Learning Scheme for Semisupervised Vehicle Type Classification

Li Guo et al.

Summary: This article proposes a semisupervised vehicle type classification scheme via ensemble broad learning for intelligent transportation systems. It achieves superior generalization performance by training a collection of base classifiers and constructing a dynamic ensemble structure to determine vehicle types.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Computer Science, Information Systems

Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification

Jianwen Mo et al.

Summary: A semi-supervised classification algorithm involving weighted pseudo labeled data and mutual learning is proposed to improve classification performance and rectify incorrect pseudo labels. By utilizing selection, weighting, and mutual learning strategies, the accuracy of pseudo label predictions is effectively improved.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems