Sequential active learning using meta-cognitive extreme learning machine.

This paper proposes a fast and effective sequential active learning method using meta-cognitive extreme learning machine (SEAL-ELM). The proposed algorithm consists of two components, namely the cognitive component and the meta-cognitive component. The cognitive component is an online sequential extreme learning machine while the meta-cognitive component controls the learning process of the cognitive component using a self-regulating mechanism to decide what to learn, when to learn and how to learn the arriving samples. Active learning is employed to select different strategies, namely sample deletion, sample reserve and sample learning strategy to determine whether the data will be deleted directly, reserved for later use or used immediately. This is the first time the similarity of meta-cognitive machine learning and active learning is exploited. The meta-cognition mechanism and active learning principle are utilized to reduce the labeling efforts and costs. The use of ELM greatly reduces the computation complexity of the learning process. The new algorithm is evaluated on a set of real-world benchmark classification problems. Simulation results demonstrate the usefulness and effectiveness of sequential active learning and show that the SEAL-ELM can achieve similar or better learning accuracy with a much faster learning speed compared with the state-of-the-arts algorithms.