### Abstract

Original language | English |
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Title of host publication | Advanced techniques in knowledge discovery and data mining |

Publisher | Springer |

Pages | 153-175 |

Number of pages | 23 |

ISBN (Print) | 1852338679 |

DOIs | |

Publication status | Published - 1 Jan 2005 |

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### Cite this

*Advanced techniques in knowledge discovery and data mining*(pp. 153-175). Springer. https://doi.org/10.1007/1-84628-183-0_6

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*Advanced techniques in knowledge discovery and data mining.*Springer, pp. 153-175. https://doi.org/10.1007/1-84628-183-0_6

**Using cooperative coevolution for data mining of Bayesian networks.** / WONG, Man Leung; LEE, Shing Yan; LEUNG, Kwong Sak.

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

TY - CHAP

T1 - Using cooperative coevolution for data mining of Bayesian networks

AU - WONG, Man Leung

AU - LEE, Shing Yan

AU - LEUNG, Kwong Sak

PY - 2005/1/1

Y1 - 2005/1/1

N2 - Bayesian networks are formal knowledge representation tools that provide reasoning under uncertainty. The applications of Bayesian networks are widespread, including data mining, information retrieval, and various diagnostic systems. Although Bayesian networks are useful, the learning problem, namely to construct a network automatically from data, remains a difficult problem. Recently, some researchers have adopted evolutionary computation for learning. However, the drawback is that the approach is slow. In this chapter, we propose a hybrid framework for Bayesian network learning. By combining the merits of two different learning approaches, we expect an improvement in learning speed. In brief, the new learning algorithm consists of two phases: the conditional independence (CI) test phase and the search phase. In the CI test phase, we conduct dependency analysis, which helps to reduce the search space. In the search phase, we perform model searching using an evolutionary approach, called cooperative coevolution. When comparing our new algorithm with an existing algorithm, we find that our algorithm performs faster and is more accurate in many cases.

AB - Bayesian networks are formal knowledge representation tools that provide reasoning under uncertainty. The applications of Bayesian networks are widespread, including data mining, information retrieval, and various diagnostic systems. Although Bayesian networks are useful, the learning problem, namely to construct a network automatically from data, remains a difficult problem. Recently, some researchers have adopted evolutionary computation for learning. However, the drawback is that the approach is slow. In this chapter, we propose a hybrid framework for Bayesian network learning. By combining the merits of two different learning approaches, we expect an improvement in learning speed. In brief, the new learning algorithm consists of two phases: the conditional independence (CI) test phase and the search phase. In the CI test phase, we conduct dependency analysis, which helps to reduce the search space. In the search phase, we perform model searching using an evolutionary approach, called cooperative coevolution. When comparing our new algorithm with an existing algorithm, we find that our algorithm performs faster and is more accurate in many cases.

UR - http://commons.ln.edu.hk/sw_master/4095

U2 - 10.1007/1-84628-183-0_6

DO - 10.1007/1-84628-183-0_6

M3 - Book Chapter

SN - 1852338679

SP - 153

EP - 175

BT - Advanced techniques in knowledge discovery and data mining

PB - Springer

ER -