SVM-RF-based decision rule extraction and reduction method

Abstract

The invention discloses an SVM-RF-based decision rule extraction and reduction method, and belongs to the technical field of computer and information science. The method comprises the following stepsof training an SVM by using data to obtain a classifier and a support vector; generating new data characteristics by adopting a regeneration tree method, obtaining a new data label by using an SVM (support vector machine), and integrating the new data to obtain a most information amount of data set; training a random forest model by using the data set with the maximum information amount to obtaina plurality of decision-making trees; fusing the terminal node similarity and the decision tree performance similarity of the decision tree into new similarity by introducing a trade-off factor, and reducing the redundant decision tree based on the similarity; and finally obtaining a rule set by using a decision tree traversal method. The decision rule extraction and reduction method provided by the invention not only gives consideration to higher accuracy of the SVM-RF model, but also can avoid that the extracted decisions are too many and are not easy to understand by people, thereby helpingthe SVM-RF model to popularize in practical application and playing a role in assisting human decision making.

Description

technical field [0001] The invention relates to a method for extracting and reducing decision rules based on SVM-RF, belonging to the fields of computer science and information technology. Background technique [0002] Rule extraction is to supplement the excellent performance of the black box model with an understandable rule set, expressing the hidden logical rules in the model in a way that is easy for humans to understand. In current scientific research, many decision-making classification models based on machine learning methods are called black boxes, that is, systems that hide their internal logic from users. Although this type of model has high performance, it cannot be used in real life because it will cause moral problems and unclear accountability mechanisms when using a decision-making model with an unclear internal logic mechanism in real life. Therefore, it has become an urgent problem to realize the rule extraction of such models to enhance the interpretabili...

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Also some trees differ only in topology but use the same feature space

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