Online Learning Method for Class Imbalanced and Feature Evolvable Streams
Feature evolvable streams are data streams in which all forms of feature spaces change dynamically,and an imbalanced class distribution may exist simultaneously.These problems may pose significant challenges to data stream classification.Online learning is an effective tool for mining data streams;however,few frameworks can handle both feature evolution and class-imbalance problems.Therefore,this study proposes an online learning method for class imbalanced and feature evolvable streams.First,by dividing the feature space of an instance,the classifiers are projected onto the corresponding feature spaces.Different classifiers are trained by combining the online passive-active algorithm.Subsequently,the cost-sensitive index minimization problem is integrated into the online optimization objective function of the model.By defining a new cost-sensitive factor according to the imbalance rate,the class weight is dynamically adjusted to solve the class imbalance problem.Finally,the important features are screened using the coefficient of variation,and an improved projection and truncation strategy is conducted to sparsify the classification model.The experimental results show that the proposed method achieved high accuracy,Geometric mean(G-mean),and Matthews Correlation Coefficient(MCC)values on 11 UCI datasets,with average improvements of approximately 0.021,0.058,and 0.072,respectively.This verifies that the proposed method has good adaptive ability to feature evolvable streams and can effectively deal with the class imbalanced problem in this type of data stream.
data streams miningfeature evolutionclass imbalanceonline learningcost sensitive learning
万方数据
维普
