A new approach for data stream classification: unsupervised feature representational online sequential extreme learning machine


MULTIMEDIA TOOLS AND APPLICATIONS, vol.79, pp.27205-27227, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 79
  • Publication Date: 2020
  • Doi Number: 10.1007/s11042-020-09300-y
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, FRANCIS, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.27205-27227
  • Keywords: Data stream, Data stream classification, Online sequential extreme learning machine, Concept drift, Extreme learning machine based autoencoder, Representational learning, CONCEPT DRIFT, CLASS IMBALANCE, INPUT WEIGHTS, ENSEMBLE, ALGORITHM, ROBUST, ELM
  • Karadeniz Technical University Affiliated: Yes


The characteristics of the data stream have brought enormous challenges to classification algorithms. Concept drift is the most concerning characteristics, and developed classification algorithms must tackle the concept drift problem. Therefore, Extreme Learning Machines (ELM) based algorithms have been developed to respond to the characteristics of the data stream. However, due to randomly assigned input layer weights, ELM based algorithms have encountered problems such as producing inconsistent outputs, generating ill-conditioned matrix, and mapping the inputs to the worst representative space. To overcome these problems, this paper aims to build a stable and well-constructed classifier that responds to the requirements of the data stream by considering all characteristics. A novel data stream classification approach based online sequential ELM (OS-ELM) with unsupervised feature representation learning (UFROS-ELM) and ensemble UFROS-ELM approach based on majority learning is presented in this paper. UFROS-ELM is a modification of the OS-ELM with ELM-AE and concept drift mechanism. ELM-AE is utilized for computing the best discriminative input weights of the classifier. The classifier is then initialized by using the determined weights, first chunk, and OS-ELM algorithm. When a new data chunk arrives, the classifier firstly searches any concept drift occurrence. If it is detected, ELM-AE is utilized to reconstruct the classifier to adapt to the changes. Otherwise, the classifier is sequentially updated updates by processing the current chunk. The results are achieved on the well-known real and artificial data sets and compared with state-of-the-art data stream classification algorithms. The experimental studies demonstrate the achievements of the proposed approaches.