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| An improved predicate invention method based on SCAD |
| 1. School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, China; 2. Southeast University-Monash University Joint Graduate School(Suzhou), Suzhou, Jiangsu 215000, China |
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Abstract To solve the problem of error cascades of traditional predicate invention method in inductive logic programming (ILP), an improved approach was proposed based on smoothly clipped absolute deviation penalty (SCAD) regularized sparsity. Instead of explicitly creating new predicates, the predicate invention method implicitly grouped closely-related rules by regularized sparsity to regularize the parameters together. The regularized sparse model of SCAD was introduced into the soft predicate invention. For the unbiased sparseness, the influence of SCAD on soft predicate invention results was analyzed. The experiments were completed based on the dataset of European royalty family ties, and the values of μ and α were determined to improve the knowledge base. The results show that the proposed approach can effectively overcome the difficulty of error cascades, and the method can improve the mean average precision in predicate invention and shorten the query time of knowledge base. The mean average precision of the approach based on SCAD regularized sparsity is 0.798, which is more higher than the soft predicate invention method of 0.726 based on Laplacian regularization.
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Received: 30 July 2017
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2018, Vol. 39 
