Simulation of Unbalanced Data Classification and Grading Algorithm Based on Relief Algorithm
In general,the classification of imbalanced data is an indispensable step in ensuring the efficient use of big data technology.However,the classification process is easily disrupted by some issues such as data attributes,redundancy and imbalance.To address this,a hierarchical algorithm for imbalanced data based on naive Bayes was proposed.Firstly,the synthetic minority oversampling technique was employed to reduce the degree of data imbalance.Then,the distance coefficient and the maximum information coefficient were used to carry out the feature selection and screening of imbalanced data.Following this,the Relief algorithm was used to assign weights of the selected features,which were then input into the naive Bayes model for classification.Finally,a dynamic threshold algorithm was utilized to complete the data classification.Experimental results prove that the proposed algorithm has a short running time and high classification accuracy,thus effectively improving data processing performance.
Unbalance degreeDistance coefficientFeature matrixWeight allocationPosterior probability
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