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Shitong Wang

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6 papers

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EAAI Journal 2026 Journal Article

Component-wise independent adaptive learning and local optimization for long-term forecasting

Fei Chen
Ke Cheng
Shitong Wang
Yuanquan Wang

Long-term time series forecasting (LTSF) faces significant challenges on small datasets due to overfitting, inconsistent training progress across model layers, and lack of interpretability. To address these issues, we propose Component-wise Independent Adaptive Learning and Local Optimization (CIALLO), a novel parallel forecasting framework that decomposes time series into reversible components — trend, waveform, and amplitude — allowing for independent modeling and targeted training. Highlights benefits of modularization: flexible sub-model selection, independent pre-training, clearer convergence analysis, and higher training efficiency. Emphasizes structural interpretability via decomposition and component-wise optimization rather than post-hoc attention. Experiments on benchmark Electricity Transformer Temperature (ETT) datasets and Traffic truncated dataset demonstrate that CIALLO achieves comparable or competitive performance with state-of-the-art models, particularly on long-term horizons and under limited data conditions. Ablations on designed modules show that lightweight sub-models and independent component training improve optimization stability, while guided gradient has minimal impact on final performance. Decomposition ablations indicate that detrending dominates while amplitude is beneficial only when scaling is reliable. Ablation on the amplitude adjustment reveals a stable U-shaped behavior, with moderate values giving the most balanced correction. Component-wise contributions and early-stopping behavior are analyzed, revealing inconsistent training progress across components. Training-time analysis also shows faster overall convergence compared to baselines. The error contribution across samples, representative prediction cases and the parameters of the designed sub-models are visualized and analyzed. Finally, the overall results are summarized and their implications for future model design and interpretability are discussed.

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AAAI Conference 2019 Conference Paper

Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data

Peng Xu
Zhaohong Deng
Kup-Sze Choi
Longbing Cao
Shitong Wang

Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features simultaneously. To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i. e. , multi-view information-theoretic co-clustering (MV-ITCC). The proposed method realizes two-sided clustering for co-occurring multi-view data under the formulation of information theory. More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension. In addition, the mechanism of maximum entropy is also adopted to control the importance of different views, which can give a right balance in leveraging the agreement and disagreement. Extensive experiments are conducted on text and image multiview datasets. The results clearly demonstrate the superiority of the proposed method.

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TIST Journal 2016 Journal Article

Enhanced Knowledge-Leverage-Based TSK Fuzzy System Modeling for Inductive Transfer Learning

Zhaohong Deng
Yizhang Jiang
Hisao Ishibuchi
Kup-Sze Choi
Shitong Wang

The knowledge-leverage-based Takagi--Sugeno--Kang fuzzy system (KL-TSK-FS) modeling method has shown promising performance for fuzzy modeling tasks where transfer learning is required. However, the knowledge-leverage mechanism of the KL-TSK-FS can be further improved. This is because available training data in the target domain are not utilized for the learning of antecedents and the knowledge transfer mechanism from a source domain to the target domain is still too simple for the learning of consequents when a Takagi--Sugeno--Kang fuzzy system (TSK-FS) model is trained in the target domain. The proposed method, that is, the enhanced KL-TSK-FS (EKL-TSK-FS), has two knowledge-leverage strategies for enhancing the parameter learning of the TSK-FS model for the target domain using available information from the source domain. One strategy is used for the learning of antecedent parameters, while the other is for consequent parameters. It is demonstrated that the proposed EKL-TSK-FS has higher transfer learning abilities than the KL-TSK-FS. In addition, the EKL-TSK-FS has been further extended for the scene of the multisource domain.

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AIIM Journal 2014 Journal Article

Transductive domain adaptive learning for epileptic electroencephalogram recognition

Changjian Yang
Zhaohong Deng
Kup-Sze Choi
Yizhang Jiang
Shitong Wang

Objective Intelligent recognition of electroencephalogram (EEG) signals is an important means for epilepsy detection. Almost all conventional intelligent recognition methods assume that the training and testing data of EEG signals have identical distribution. However, this assumption may indeed be invalid for practical applications due to differences in distributions between the training and testing data, making the conventional epilepsy detection algorithms not feasible under such situations. In order to overcome this problem, we proposed a transfer-learning-based intelligent recognition method for epilepsy detection. Methods We used the large-margin-projected transductive support vector machine method (LMPROJ) to learn the useful knowledge between the training domain and testing domain by calculating the maximal mean discrepancy. The method can effectively learn a model for the testing data with training data of different distributions, thereby relaxing the constraint that the data distribution in the training and testing samples should be identical. Results The experimental validation is performed over six datasets of electroencephalogram signals with three feature extraction methods. The proposed LMPROJ-based transfer learning method was compared with five conventional classification methods. For the datasets with identical distribution, the performance of these six classification methods was comparable. They all could achieve an accuracy of 90%. However, the LMPROJ method obviously outperformed the five conventional methods for experimental datasets with different distribution between the training and test data. Regardless of the feature extraction method applied, the mean classification accuracy of the proposed method is above 93%, which is greater than that of the other five methods with statistical significance. Conclusion The proposed transfer-learning-based method has better classification accuracy and adaptability than the conventional methods in classifying EEG signals for epilepsy detection.

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AIIM Journal 2013 Journal Article

Weighted spherical 1-mean with phase shift and its application in electrocardiogram discord detection

Jun Wang
Fu-lai Chung
Zhaohong Deng
Shitong Wang
Wenhao Ying

Objective Detecting discords in time series is a special novelty detection task that has found many interesting applications. Unlike the traditional novelty detection methods which can make use of a separate set of normal samples to build up the model, discord detection is often provided with mixed data containing both normal and abnormal data. The objective of this work is to present an effective method to detect discords in unsynchronized periodic time series data. Methods The task of discord detection is considered as a problem of unsupervised learning with noise data. A new clustering algorithm named weighted spherical 1-mean with phase shift (PS-WS1M) is proposed in this work. It introduces a phase adjustment procedure into the iterative clustering process and produces a set of anomaly scores based upon which an unsupervised approach is employed to locate the discords automatically. A theoretical analysis on the robustness and convergence of PS-WS1M is also given. Results The proposed algorithm is evaluated via real-world electrocardiograms datasets extracted from the MIT-BIH database. The experimental results show that the proposed algorithm is effective and competitive for the problem of discord detection in periodic time series. Meanwhile, the robustness of PS-WS1M is also experimentally verified. As compared to some of the other discord detection methods, the proposed algorithm can always achieve ideal FScore values with most of which exceeding 0. 98. Conclusion The proposed PS-WS1M algorithm allows the integration of a phase adjustment procedure into the iterative clustering process and it can be successfully applied to detect discords in time series.

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AIIM Journal 2007 Journal Article

Advanced fuzzy cellular neural network: Application to CT liver images

Shitong Wang
Duan Fu
Min Xu
Dewen Hu

Objective To achieve better boundary integrities and recall accuracies for segmented liver images, use of the advanced fuzzy cellular neural network (AFCNN), as a variant of the fuzzy cellular neural network (FCNN), is proposed to effectively segment CT liver images. Materials and methods In order to better utilize relevant contour and gray information from liver images, we have improved the FCNN [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], which proved to be very effective for the segmentation of microscopic white blood cell images, to create the novel neural network, AFCNN. Its convergent property and global stability are proved. Based on the FCNN-based NDA algorithm [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], we developed the AFCNN-based NDA algorithm, which we used to segment 5 CT liver images. For comparison, we also segmented the same 5 CT liver images using the FCNN-based NDA algorithm. Results and conclusion: AFCNN has distinct advantages over FCNN in both boundary integrity and recall accuracy. In particular, the performance index Binary_rate is generally much higher for AFCNN than for FCNN when applied to CT liver images.

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