Cyclic update mode-based decision tree construction method

Abstract

The present invention relates to a cyclic update mode-based decision tree construction method. The method comprises: calculating a Bayesian probability by using a target function Pareto optimal value based on time and spatial complexity of the decision tree and the Bayesian theory; modifying a given decision table according to the Bayesian probability; constructing, by using a decision tree selection standard-based calculation method, a decision tree in which a decision table is modified; and applying the decision tree in an information system for automatic decision making, and recording the decision making process. After the system runs for a period of time, a ratio of the automatic decision times of each row in a corresponding decision table to the total decision times of all the rows in the decision table is calculated according to the data of the recorded decision making process, the decision table is modified according to the ratio, a new decision tree of the modified decision table is constructed by using the decision tree selection standard-based calculation method, and the new decision tree is re-applied to the information system. such steps are repeatedly performed until the calculated ratio of each row is less than a decision change probability threshold. The information system employing the technical solution according to the present invention has high-efficiency automatic decision function.

Description

Technical field [0001] The invention relates to a decision tree construction method based on a cyclic update mode. Background technique [0002] There are many calculation methods used to build decision trees. Most of these algorithms focus on the complexity of decision trees such as the number of decision tree endpoints and the height of the decision tree. The types can be roughly divided into dynamic programming, incremental algorithms, and greedy algorithms. Wait. According to the research of S. Hussain, "Total path length and number of terminal nodes for decision trees", Procedia Comput. Sci., vol. 35, pp. 514-521, 2014. For general decision tables, it is usually impossible to construct all Decision tree with minimal complexity. This situation where all the complexity cannot be minimized belongs to the category of multi-objective optimization theory. However, unlike the feasible space in multi-objective optimization, which can be explicitly explored, the feasible space of t...

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Problems solved by technology

This method of modifying the decision table can neither guarantee the integrity of the information in the decision table nor can it further optimize the decision tree according to the actual application.

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