This paper proposes a twin-type extreme learning machine based on the weighted linear loss function and projection technique called the weighted linear loss projection twin extreme learning machine (WLPTELM). By introducing the weighted linear loss function, the proposed WLPTELM balances the impact of each sample point on the model performance. By using the projection technique, more discriminant information can be retained and better classification performance can be obtained, which can effectively deal with linearly inseparable problems. Furthermore, it only needs to solve a pair of systems of linear equations, which not only improves the computational efficiency but also ensures the generalization ability of the model. Hence WLPTELM can handle large-scale binary classification problems simply and efficiently. Numerical experiments on artificial datasets and a number of benchmark datasets demonstrated the practicability and validity of the proposed WLPTELM.
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