Classification method and device for multi-level classification objects

Abstract

The invention discloses a classification method and device for multi-level classification objects, and relates to the technical field of computers. One specific embodiment of the method comprises thefollowing steps: constructing a cascade classification model step by step in a joint training mode by utilizing sample feature data of multi-stage classification objects, and a construction process: constructing a first-stage cascade classification model according to a first-stage sub-classifier; when K is larger than or equal to 2 and smaller than or equal to N, the sample feature data of the multi-level classification objects and K-1-level classification information, output by the K-1-level cascade classification model, of the multi-level classification objects are jointly input into a Kth-level sub-classifier, so that the K-1-level cascade classification model and the Kth-level sub-classifier are jointly trained, and a K-level cascade classification model is obtained; and classifying the to-be-classified data of the multi-stage classification object by utilizing the final N-stage cascade classification model to obtain N-stage classification information. According to the embodiment,the inter-class hydrophilicity and hydrophobicity are considered, the classification effect is good, error transfer can be avoided, the classification accuracy is improved, the overall model complexity is low, and the model development difficulty and workload are reduced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a classification method and device for multi-level classification objects. Background technique [0002] Multi-level classification refers to a multi-classification problem in which the category itself has a hierarchy. The biggest feature of multi-level classification is the obvious relationship between categories. For example, organisms are divided into animals and plants, animals are divided into chordates and non-chordates, plants are divided into bryophytes, ferns, etc., among which chordates and Inchords are more closely related, and bryophytes are more distant. Existing multi-level classification schemes: Scheme 1 is to use conventional machine learning models, such as support vector machines, neural networks, naive Bayesian, decision trees, etc., which consider that there is no relationship between categories, and do not consider the relationship between categories; Th...

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Problems solved by technology

[0005] In the existing technology, scheme one does not consider the affinity between classes, and the classification effect is poor; scheme two has cumulative errors, poor classification accuracy, heavy modeling workload, and high model complexity

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