Abstract
Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.
Highlights
The birth of the first computer in 1946 marked the creation of a real tool with which humans were able to simulate human thinking
The structure learning method based on the artificial bee colony algorithm (ABC) [21] treats the process of learning Bayesian network structure from a data set as the process of a bee colony searching for a food source
On the basis of a multi-group artificial bee colony algorithm based on cuckoo algorithm (CMABC) and knowledge of Bayesian networks, this paper proposed a Bayesian network structure learning method based on discrete CMABC algorithm (CMABC-BNL)
Summary
The birth of the first computer in 1946 marked the creation of a real tool with which humans were able to simulate human thinking. By collecting the experience and knowledge of experts in various fields, summing these up as operating rules, and inputting the obtained rules to a computer, computers are able to solve problems encountered in the field. This rule-based expert system does not have the ability to learn. The great advantages of Bayesian networks in solving uncertainty problems have made them central to the study of intelligence, data mining, and machine learning, as well as to many fields involving decision-making, diagnosis, and evaluation [2]. Learning the Bayesian network structure from the data becomes a non-deterministic polynomial (NP)-hard [4] question. One of the main challenges of Bayesian network research Structure is to find anLearning effective structure learning method to learn an optimal structure from
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