Active broad learning with multi-objective evolution for data stream classification
Abstract In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts.Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for l