The invention discloses an self-adaptive integrated
unbalanced data classification method based on
Euclidean distance, which comprises the following steps of: firstly, obtaining a plurality of diversified balance subsets by using a random balance method, then establishing and obtaining a plurality of basic classifiers on each balance subset; and adding a classifier pre-
selection algorithm before the dynamic
selection algorithm. After a screened basic classifier is obtained, a new dynamic
selection algorithm is provided, and by evaluating the condition of the sample classifier in the surrounding area of a to-be-classified sample, the capability is stronger when more
minority class samples belong to the correct classification range. And finally, a prediction result obtained by the selected basic classifier by adopting a distance-based
adaptive integration rule is output. According to the method, basic classifiers can be established on the generated diversified subsets, meanwhile, a dynamic selection
algorithm is provided, the sub-classifier with the highest classification capacity can be selected out, finally, the proposed
integration rule can provide a better output result, and finally, the
unbalanced data classification precision is effectively improved.