6 results on '"Pan, Xiaoying"'
Search Results
2. A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery.
- Author
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Zhang, Wuxia, Zhang, Qinyu, Liu, Shuo, Pan, Xiaoying, and Lu, Xiaoqiang
- Subjects
MACHINE learning ,MARKOV random fields - Abstract
Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers' attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. User Clustering Topic Recommendation Algorithm Based on Two Phase in the Social Network
- Author
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Li Pei, Pan Xiaoying, and Chen Hao
- Subjects
Fuzzy clustering ,General Computer Science ,Computer science ,business.industry ,Correlation clustering ,Constrained clustering ,Machine learning ,computer.software_genre ,Determining the number of clusters in a data set ,Data stream clustering ,CURE data clustering algorithm ,Canopy clustering algorithm ,Artificial intelligence ,Data mining ,business ,Cluster analysis ,computer ,Algorithm - Abstract
To deal with the issues like existing common data sparseness in weibo social network and the phenomena of cold start, this paper puts forward a two-stage clustering based on the recommendation algorithm GCCR. The algorithm firstly selects users’ focused nodes which have higher number, so as to extract a dense subset of sparse data, and by using the method of graph paper, similar concerned interested core clustering is formed to this dense subset. Then, it is extracted that weibo content features of seed clustering and the whole data set other users. Then the entire user group is clustered based on content similarity. Finally the clustering results are used in subject recommendation. Through clustering the two phases of dense data subset and the whole data set, the clustering effect of extreme sparse data sets are improved. At the same time, because of fuzziness of graph clustering, this thesis retains a certain diversity in the process of user interest clustering, so as to avoid convergence too fast when cold start. This method is verified through the real social network data, and the experimental results show that this algorithm can effectively solve the problems such as data sparseness and cold start phenomenon.
- Published
- 2015
4. Dosimetric predictors of patient-reported toxicity after prostate stereotactic body radiotherapy: Analysis of full range of the dose–volume histogram using ensemble machine learning.
- Author
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Pan, Xiaoying, Levin-Epstein, Rebecca, Huang, Jiahao, Ruan, Dan, King, Christopher R., Kishan, Amar U., Steinberg, Michael L., and Qi, X. Sharon
- Subjects
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STEREOTACTIC radiotherapy , *MACHINE learning , *PROSTATE , *VOLUMETRIC-modulated arc therapy , *STEREOTAXIC techniques , *TREATMENT effectiveness , *URINARY incontinence - Abstract
• Machine learning methods were used to assess dosimetric associations with patient-reported outcomes. • Full range of dosimetric quantities demonstrated predictive ability for patient-reported toxicities after prostate SBRT. • Dosimetric predictors with patient-reported toxicities were identified using advanced ML methods. • Predictive DVH metrics can be employed to refine planning guidelines to further reduce toxicity. • Outcome-driven treatment planning may further improve treatment outcome. This study aims to evaluate the associations between dosimetric parameters and patient-reported outcomes, and to identify latent dosimetric parameters that most correlate with acute and subacute patient-reported urinary and rectal toxicity after prostate stereotactic body radiotherapy (SBRT) using machine learning methods. Eighty-six patients who underwent prostate SBRT (40 Gy in 5 fractions) were included. Patient-reported health-related quality of life (HRQOL) outcomes were derived from bowel and bladder symptom scores on the Expanded Prostate Cancer Index Composite (EPIC-26) at 3 and 12 months post-SBRT. We utilized ensemble machine learning (ML) to interrogate the entire dose–volume histogram (DVH) to evaluate relationships between dose–volume parameters and HRQOL changes. The latent predictive dosimetric parameters that were most associated with HRQOL changes in urinary and rectal function were thus identified. An external cohort of 26 prostate SBRT patients was acquired to further test the predictive models. Bladder dose–volume metrics strongly predicted patient-reported urinary irritative and incontinence symptoms (area under the curves [AUCs] of 0.79 and 0.87, respectively) at 12 months. Maximum bladder dose, bladder V102.5%, bladder volume, and conformity indices (V50/VPTV and V100/VPTV) were most predictive of HRQOL changes in both urinary domains. No strong rectal toxicity dosimetric association was identified (AUC = 0.64). We demonstrated the application of advanced ML methods to identify a set of dosimetric variables that most highly correlated with patient-reported urinary HRQOL. DVH quantities identified with these methods may be used to achieve outcome-driven planning objectives to further reduce patient-reported toxicity with prostate SBRT. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
5. A granular agent evolutionary algorithm for classification.
- Author
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Pan, Xiaoying and Jiao, Licheng
- Subjects
GRANULAR computing ,ALGORITHMS ,EVOLUTIONARY computation ,CLASSIFICATION ,DATA mining ,MACHINE learning - Abstract
Abstract: By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed. The method uses the granular agent to denote the set of some examples that have similar attributions and the knowledge base guides the evolution of granular agent. Also some granular evolutionary operators are designed for classification problem. Assimilation operator, exchange operator, and differentiation operator reflect the competitive, cooperative and self-learning ability of agent, respectively. Finally, some classification rules are extracted from granular agents by some strategy to forecast the sort of new data. Empirical study contains UCI data sets, KDDCUP99 data sets and remote image recognition. The test results show that the algorithm has a good classification prediction, and only need a small price for the training time. In most UCI data sets, the performance of GAEC is better than G-NET, OCEC and C4.5, which have good performance. At the same time, some Gaussian White Noise attributes are added to these UCI data sets and the results show GACE has some anti-noise abilities. To test the scalability of GAEC, two functions along two dimensions, the number of training examples and the number of attributes are used. Also, GAEC are applied to some real world fields, intrusion detection system and remote sensing image recognition. The experiments for KDDCUP99 verify GAEC has capability to deal with massive data in real world and good predicting capability for unknown type data. At last, the accuracy rate of GAEC is also good for the remote sensing image recognition. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
6. Two-step ensemble under-sampling algorithm for massive imbalanced data classification.
- Author
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Bai, Lin, Ju, Tong, Wang, Hao, Lei, Mingzhu, and Pan, Xiaoying
- Subjects
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CLASSIFICATION , *ALGORITHMS , *MACHINE learning , *PERFORMANCES , *DATA distribution - Abstract
Imbalanced data classification is a challenging problem in the field of machine learning. Class imbalance, class overlap, and large data volume significantly affect classification performance. Focusing on the impact of class overlap on classification effectiveness, we propose a two-step ensemble under-sampling algorithm based on boundary information mining (TSSE-BIM) with the goal of reducing the information loss from under-sampling methods on large-scale imbalanced data. In the first stage, the proposed method applies an improved equalization under-sampling strategy to mine sample contribution information and quickly obtains the distribution information of data relative to the decision boundary. In the second stage, based on the boundary information, a weighted boundary sampling is performed to remove noisy and highly overlapping samples. It is easy to retain samples with high contribution and effectively suppress the information loss caused by under-sampling. Then, the overall framework is designed based on a serial ensemble similar to boosting, where the weights of each base classifier are assigned to achieve a more powerful performance based on the false positive rate and false negative rate on the original data. Finally, extensive experiments indicate that TSSE-BIM outperforms state-of-the-art methods and ranks first on average under four metrics, especially F1 and MCC. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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