Abstract
Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated blocks. Then, a pruned dynamic programming algorithm is performed sequentially for all possible graph structures to get possible BNs and find the best BN by scoring function. Experiments show that for high-dimensional and sparse data, the BLMKM algorithm can achieve the same accuracy in a reasonable time compared with non-blocking classical learning algorithms. Compared to the existing block learning algorithms, the BLMKM algorithm has a time advantage on the basis of ensuring accuracy. The analysis of the real radar effect mechanism dataset proves that BLMKM algorithm can quickly establish a global and accurate causality model to find the cause of interference, predict the detecting result, and guide the parameters optimization. BLMKM algorithm is efficient for BN learning and has practical application value.
Highlights
With the rapid development of machine learning theory, Bayesian Networks (BN) as a powerful causal representation model were proposed by Judea Pearl in 1986 [1], which can systematically describe the causality between random variables
The organizational structure of this paper is as follows: Section 2 briefly introduces the relevant theories of BN structure learning and network block; Section 3 details the proposed BLMKM algorithm and prediction method, including the idea of a BLMKM algorithm and the related algorithms involved in a BLMKM algorithm; Section 4 performs simulation experiments to verify the performance of BLMKM algorithm and uses BLMKM algorithm for analysis of radar effect mechanism
This paper proposes the BLMKM algorithm for BN modeling of high-dimensional data, which can be summarized as the following steps: In the first step, the data is blocked by the proposed MKM algorithm; in the second step, the minimum parents and children (MMPC) algorithm is used to determine the network skeleton; in the third step, the combine function assumes the direction of the edges between the blocks to find the possible graph structures between all the blocks
Summary
With the rapid development of machine learning theory, Bayesian Networks (BN) as a powerful causal representation model were proposed by Judea Pearl in 1986 [1], which can systematically describe the causality between random variables. The above algorithms are designed to solve the BN structural learning of high-dimensional data when the number of nodes increases, but because the learning algorithm still uses approximate learning algorithms, there is no guarantee that the final obtained model will be optimal At this point, it may be a local optimal network or the accuracy may be limited. The organizational structure of this paper is as follows: Section 2 briefly introduces the relevant theories of BN structure learning and network block; Section 3 details the proposed BLMKM algorithm and prediction method, including the idea of a BLMKM algorithm and the related algorithms involved in a BLMKM algorithm; Section 4 performs simulation experiments to verify the performance of BLMKM algorithm and uses BLMKM algorithm for analysis of radar effect mechanism.
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