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20 results on '"Fong, Alvis C. M."'

2. Web-Based Mathematics Testing with Automatic Assessment

Author

Nguyen, Minh Luan, Hui, Siu Cheung, Fong, Alvis C. M., 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, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Anthony, Patricia, editor, Ishizuka, Mitsuru, editor, and Lukose, Dickson, editor

Published
2012
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3. Content-Based Collaborative Filtering for Question Difficulty Calibration

Author

Nguyen, Minh Luan, Hui, Siu Cheung, Fong, Alvis C. M., 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, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Anthony, Patricia, editor, Ishizuka, Mitsuru, editor, and Lukose, Dickson, editor

Published
2012
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4. A Divide-and-Conquer Tabu Search Approach for Online Test Paper Generation

Author

Nguyen, Minh Luan, Hui, Siu Cheung, Fong, Alvis C. M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wang, Dianhui, editor, and Reynolds, Mark, editor

Published
2011
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6. Associative Classification with Prediction Confidence

Author

Do, Tien Dung, Hui, Siu Cheung, Fong, Alvis C. M., 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, Yeung, Daniel S., editor, Liu, Zhi-Qiang, editor, Wang, Xi-Zhao, editor, and Yan, Hong, editor

Published
2006
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7. Mining Class Association Rules with Artificial Immune System

Author

Do, Tien Dung, Hui, Siu Cheung, Fong, Alvis C. M., 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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8. Artificial Immune System for Associative Classification

Author

Do, Tien Dung, Hui, Siu Cheung, Fong, Alvis C. M., 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, Wang, Lipo, editor, Chen, Ke, editor, and Ong, Yew Soon, editor

Published
2005
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9. Mining Association Rules Using Relative Confidence

Author

Do, Tien Dung, Hui, Siu Cheung, Fong, Alvis C. M., 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, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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19. Associative Classification with Prediction Confidence.

Author

Yeung, Daniel S., Zhi-Qiang Liu, Xi-Zhao Wang, Hong Yan, Tien Dung Do, Siu Cheung Hui, and Fong, Alvis C. M.

Abstract

Associative classification which uses association rules for classification has achieved high accuracy in comparison with other classification approaches. However, the confidence measure which is conventionally used for selecting association rules for classification may not conform to the prediction accuracy of the rules. In this paper, we propose a measure called prediction confidence to measure the prediction accuracy of association rules. In addition, a probabilistic-based approach for estimating prediction confidence of association rules is given and its performance is evaluated. The use of prediction confidence helps improve the performance of associative classifiers. [ABSTRACT FROM AUTHOR]

Published
2006
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20. Mining Class Association Rules with Artificial Immune System.

Author

Khosla, Rajiv, Howlett, Robert J., Jain, Lakhmi C., Tien Dung Do, Siu Cheung Hui, and Fong, Alvis C. M.

Abstract

Associative classification, which is based on association rules, has shown great promise over many other classification techniques. However, the very large search space of possible rules may cause performance degradation in the rule mining process as well as classification accuracy. In this paper, we propose a new approach known as AIS-AC, which is based on Artificial Immune System (AIS), for mining class association rules for associative classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rulesthat are suitable for effective associative classification in an evolutionary manner. [ABSTRACT FROM AUTHOR]

Published
2005
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