|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Information entropy for ordinal classification显示文摘Ordinal classification plays an important role in various decision making tasks.However, little attention is paid to this type of learning tasks compared with general classification learning.Shannon information entropy and the derived measure of mutual information play a fundamental role in a number of learning algorithms including feature evaluation, selection and decision tree construction.These measures are not applicable to ordinal classification for they cannot characterize the consistency of monotonicity in ordinal classification.In this paper, we generalize Shannon's entropy to crisp ordinal classification and fuzzy ordinal classification, and show the information measures of ranking mutual information and fuzzy ranking mutual information.We discuss the properties of these measures and show that the proposed ranking mutual information and fuzzy ranking mutual information are the indexes of consistency of monotonicity in ordinal classification.In addition, the proposed indexes are used to evaluate the monotonicity degree between features and decision in the context of ordinal classification. | HU QingHua GUO MaoZu YU DaRen LIU JinFu | 2010 | Science China(Information Sciences)2010，53，6： | 29 |
| 2 | A new Q-learning algorithm based on the metropolis criterion显示文摘 | GUO Maozu LIU Yang MALEC J | 2004 | IEEE Transactions on Systems Man and Cybernetics2004，5，34： | 1 |
| 3 | Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field显示文摘MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs. | Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu | 2018 | Journal of Harbin Institute of Technology(New Series)2018，25，1： | 1 |
| 4 | A new Q-learning algorithm based on the Metropolis criterion显示文摘 | Guo Maozu Liu Yang Malec J | 2004 | IEEE Transactions on System Man and Cybernetics-Part B:Cybernetics2004，34，5： | 1 |
| 5 | A new Q-Learning algorithm based on the metropolis criterion显示文摘 | Gun Maozu Liu Yang Malec J | 2004 | IEEE Trans on Systems Man and Cybernetics2004，34，5： | 1 |
| 6 | A New Q-Learning Algorithm Based on the Metropolis Criterion 显示文摘 | Maozu Guo Yang Liu Jacek Malec | 2004 | IEEE Transactions on Systems man and Cybernetics2004，34，5： | 1 |
| 7 | Fuzzy preference based rough sets显示文摘 | Qinghua Hu Daren Yu Maozu Guo | 2010 | Information Sciences2010，，10： | 1 |
| 8 | FastJoin,an improved neighbor-joining algorithm显示文摘 | WANG Juan GUO Maozu XING Linlin | | 0，，： | 1 |
| 9 | A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding显示文摘A common but critical task in biological ontologies data analysis is to compare the difference between ontologies.There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain,but it still remains a challenge for crossdomain ontologies comparison.An ontology contains the scientific natural language description for the corresponding biological aspect.Therefore,we develop a new method based on natural language processing(NLP)representation model bidirectional encoder representations from transformers(BERT)for cross-domain semantic representation of biological ontologies.This article uses the BERT model to represent the word-level of the ontologies as a set of vectors,facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms.We evaluated the ability of our method in two experiments:calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction.The experimental results demonstrated the comparative performance.This gives promise to the development of NLP methods in biological data analysis. | ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu | 2022 | Chinese Journal of Electronics2022，31，5： | 1 |
| 10 | Fuzzy preference based rough sets显示文摘 | Qinghua Hu Daren Yu Maozu Guo | 2010 | Information Sciences2010，180，： | 1 |
| 11 | A new Q-learning algorithm based on the metropolis criterion显示文摘 | GUO Maozu LIU Yang JACEK M | 2004 | IEEE Transactions on Systems Man and Cybernetics2004，34，5： | 1 |
| 12 | Pedestrian wind flow prediction using spatial-frequency generative adversarial network显示文摘Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions.Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations.Deep learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow.However,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images.This study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction.In the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial characteristics.Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics.These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced predictions.Experimental results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,respectively.We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow.SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings.The enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind comfort.The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost. | Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao | 2024 | Building Simulation2024，17，2： | 0 |
| 13 | Construct Protein-Protein Interaction Network by Mining Domain-Domain Interactions显示文摘Domain-domain interactions are important clues to inferring protein-protein interactions. Although about 8 000 domain-domain interactions are discovered so far,they are just the tip of the iceberg. Because domains are conservative and commonplace in proteins,domain-domain interactions are discovered based on pairs of domains which significantly co-exist in proteins. Meanwhile,it is realized that:( 1) domain-domain interactions may exist within the same proteins or across different proteins;( 2) only the domain-domain interactions across different proteins can mediate interactions between proteins;( 3) domains have biases to interact with other domains. And then,a novel method is put forward to construct protein-protein interaction network by using domain-domain interactions. The method is validated by experiments and compared with the state- of-art methods in the field. The experimental results suggest that the method is reasonable and effectiveness on constructing Protein-protein interactions network. | Zhixia Teng Maozu Guo Xiaoyan Liu Jin Li Qiguo Dai Chunyu Wang | 2016 | Journal of Harbin Institute of Technology(New Series)2016，23，4： | 0 |
| 14 | Identification and prioritization of differentially expressed genes for time-series gene expression data显示文摘 | Linlin XING Maozu GUO Xiaoyan LIU Chunyu WANG | 2018 | Frontiers of Computer Science2018，12，4： | 0 |
| 15 | Application of nonlinear color matching model to four-color ink-jet printing显示文摘Through discussing the color matching technology and its application in printing industry the conventional approaches commonly used in color matching, and the difficulties in color matching, a nonlinear color matching model based on two step learning is established by finding a linear model by learning pure color data first and then a nonlinear modification model by learning mixed color data. Nonlinear multiple regression is used to fit the parameters of the modification model. Nonlinear modification function is discovered by BACON system by learning mixture data. Experiment results indicate that nonlinear color conversion by two step learning can further improve the accuracy when it is used for straightforward conversion from RGB to CMYK. An improved separation model based on GCR concept is proposed to solve the problem of gray balance and it can be used for three to four color conversion as well. The method proposed has better learning ability and faster printing speed than other historical approaches when it is applied to four color ink jet printing. | SU Xiaohong(苏小红) ZHANG Tianwen(张田文) GUO Maozu(郭茂祖) WANG Yadong(王亚东) | 2002 | Journal of Harbin Institute of Technology(New Series)2002，9，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员