The invention relates to the field of computers, in particular to an
electric power data classification method and
system based on a naive
Bayesian algorithm, and the method comprises the steps: S1, obtaining data from an
electric power system of an
electric power company, and generating a
data set; S2, taking a data subset from the
data set, and carrying out incremental training to obtain the data subset; S3, calculating the frequency of each type of Ck in the data subset; S4, dividing the data subset into K sub-data subsets, and calculating the probability that the jth feature Xj is equal toajl; S5, calculating the
posterior probability of each category Ck, wherein the category with the maximum probability value is the prediction category of the to-be-predicted sample; S6, removing thecurrent data subset from the
data set, judging whether the data set is empty or not, if not, executing the step S2, and if yes, ending classification. According to the method, maximum likelihood
estimation is adopted to represent the probabilities of various classifications for various features, and then the category with the maximum probability value is selected as the prediction category of theto-be-predicted sample, so that
data classification can be quickly and accurately realized.