PurposeThe purpose of this paper is to investigate the food price inflation convergence across countries and regions. This study aims to identify the key drivers for food price inflation across countries and regions.Design/methodology/approachWe test whether the international food price inflations are converging over time using the log t convergence test and clustering analysis. These inflation data are collected from the Food and Agriculture Organization of the United Nations.FindingsThe test results suggest that there is little evidence of overall convergence. Then we utilize a clustering algorithm and the results support that there is strong evidence of multiple convergence clubs. In addition, we examine the transition path of the various convergence and find that social stability regulation together with economic conditions are important determinants of convergence club membership.Research limitations/implicationsFirst off, local conflict and economic environment result in food supply and prices, but this study is limited to the dynamics of prices.Practical implicationsFood prices inflations are not converging to single common price inflation, but there exist subgroups of countries or regions within which food price inflation tends to converge. These groupings tend to be related to the economic development and social stability of countries and regions.Social implicationsThe authors believe that any analysis of food price inflations that does not consider the political environment and economic conditions dynamics will likely be omitting important components of food price dynamics.Originality/valueThis study uses a unique data set covering 198 countries and regions and provides a comprehensive analysis of international food price inflation convergence identifying the key drivers of convergence club membership.
Extended belief rule-based (EBRB) system has a better ability to model complex problems than belief rule-based (BRB) system. However, the storage of rules in EBRB system is out of order, which leads to the low efficiency of rule retrieval during the reasoning process. Therefore, to improve the efficiency of rule retrieval, this study introduces K-means clustering tree algorithm into the construction of rule base, then proposes a multi-layer weighted reasoning approach based on K-means clustering tree. The proposed approach seeks out a path on the tree during the rule retrieval process, and then figures out several reasoning results according to the nodes on the path. These results are weighted and aggregated to obtain the final conclusion of the system, thus ensure both the efficiency of reasoning and the sufficient utilization of information. In addition, the differential evolution (DE) algorithm is used to train the parameters of EBRB system in this study. Several experiments are conducted on commonly used classification datasets from UCI, and the results are compared with some existing works of EBRB system and conventional machine learning methods. The comparison results illustrate that the proposed method can make an obvious improvement in the performance of EBRB system.INDEX TERMS Extended belief rule-based system, K-means clustering tree, differential evolutionary.
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