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Your search keyword '"Baoyan Liu"' showing total 11 results
Start Over Author "Baoyan Liu" Publisher springer berlin heidelberg
11 results on '"Baoyan Liu"'

1. Algorithms for Finding and Correcting Four Kinds of Data Mistakes in Information Table

Author

Honghai, Feng, Hao, Xu, Baoyan, Liu, LiYun, He, Bingru, Yang, Yueli, Li, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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2. A Discretization Algorithm That Keeps Positive Regions of All the Decision Classes

Author

Honghai, Feng, Baoyan, Liu, LiYun, He, Bingru, Yang, Yueli, Li, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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3. Using Rough Set to Induce More Abstract Rules from Rule Base

Author

Honghai, Feng, Baoyan, Liu, LiYun, He, Bingru, Yang, Yueli, Li, Shuo, Zhao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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4. An Algorithm for Eliminating the Inconsistencies Caused During Discretization

Author

Honghai, Feng, Baoyan, Liu, LiYun, He, Bingru, Yang, Yumei, Chen, Shuo, Zhao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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5. Using Rough Set to Find the Factors That Negate the Typical Dependency of a Decision Attribute on Some Condition Attributes

Author

Honghai, Feng, Hao, Xu, Baoyan, Liu, Bingru, Yang, Zhuye, Gao, Yueli, Li, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Corchado, Emilio, editor, Yin, Hujun, editor, Botti, Vicente, editor, and Fyfe, Colin, editor

Published
2006
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6. Using Positive Region to Reduce the Computational Complexity of Discernibility Matrix Method

Author

Honghai, Feng, Shuo, Zhao, Baoyan, Liu, LiYun, He, Bingru, Yang, Yueli, Li, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ali, Moonis, editor, and Dapoigny, Richard, editor

Published
2006
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7. Using Rough Set to Reduce SVM Classifier Complexity and Its Use in SARS Data Set

Author

Honghai, Feng, Baoyan, Liu, Cheng, Yin, Ping, Li, Bingru, Yang, Yumei, Chen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Khosla, Rajiv, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2005
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9. Novel Two-Stage Analytic Approach in Extraction of Strong Herb-Herb Interactions in TCM Clinical Treatment of Insomnia

Author

Baoyan Liu, Xuezhong Zhou, Josiah Poon, Paul Kwan, Simon K. Poon, Yinhui Wang, Runshun Zhang, and Daniel Sze

Subjects
High probability, Multifactor dimensionality reduction, Computer science, business.industry, High dimensional, Machine learning, computer.software_genre, complex mixtures, Epistasis, Effective treatment, Artificial intelligence, Data mining, High order, business, computer, Clinical treatment, Network analysis
Abstract

In this paper, we aim to investigate strong herb-herb interactions in TCM for effective treatment of insomnia. Given that extraction of herb interactions is quite similar to gene epistasis study due to non-linear interactions among their study factors, we propose to apply Multifactor Dimensionality Reduction (MDR) that has shown useful in discovering hidden interaction patterns in biomedical domains. However, MDR suffers from high computational overhead incurred in its exhaustive enumeration of factors combinations in its processing. To address this drawback, we introduce a two-stage analytical approach which first uses hierarchical core sub-network analysis to pre-select the subset of herbs that have high probability in participating in herb-herb interactions, which is followed by applying MDR to detect strong attribute interactions in the pre-selected subset. Experimental evaluation confirms that this approach is able to detect effective high order herb-herb interaction models in high dimensional TCM insomnia dataset that also has high predictive accuracies.

Published
2010
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10. Text Mining for Clinical Chinese Herbal Medical Knowledge Discovery

Author

Xuezhong Zhou, Baoyan Liu, and Zhaohui Wu

Subjects
medicine.medical_specialty, Medical knowledge, Knowledge management, business.industry, Computer science, Public health, food and beverages, Traditional Chinese medicine, complex mixtures, Text mining, Knowledge extraction, Health care, medicine, business, Disease treatment
Abstract

Chinese herbal medicine has been an effective therapy for healthcare and disease treatment. Large amount of TCM literature data have been curated in the last ten years, most of which is about the TCM clinical researches with herbal medicine. This paper develops text mining system named MeDisco/3T to extract the clinical Chinese medical formula data from literature, and discover the combination knowledge of herbal medicine by frequent itemset analysis. Over 18,000 clinical Chinese medical formula are acquired, furthermore, significant frequent herbal medicine pairs and the family combination rule of herbal medicine have primary been studied.

Published
2005
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11. Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

Author

Zhaohui Wu, Baoyan Liu, Junli Chen, and Xuezhong Zhou

Subjects
business.industry, Computer science, MEDLINE, Bootstrapping (linguistics), computer.software_genre, Clinical disease, Data science, Modern life, Field (computer science), Text mining, Knowledge extraction, Artificial intelligence, business, computer, Functional genomics, Natural language processing
Abstract

We present a novel text mining approach to uncover the functional gene relationships, maybe, temporal and spatial functional modular interaction networks, from MEDLINE in large scale. Other than the regular approaches, which only consider the reductionistic molecular biological knowledge in MEDLINE, we use TCM knowledge(e.g. Symptom Complex) and the 50,000 TCM bibliographic records to automatically congregate the related genes. A simple but efficient bootstrapping technique is used to extract the clinical disease names from TCM literature, and term co-occurrence is used to identify the disease-gene relationships in MEDLINE abstracts and titles. The underlying hypothesis is that the relevant genes of the same Symptom Complex will have some biological interactions. It is also a probing research to study the connection of TCM with modern biomedical and post-genomics studies by text mining. The preliminary results show that Symptom Complex gives a novel top-down view of functional genomics research, and it is a promising research field while connecting TCM with modern life science using text mining.

Published
2004
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