Abstract
Twin extreme learning machine (TELM) based on the hinge-loss function shows great potential for pattern classification. However, the hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for resampling. In contrast, the $\varepsilon $ -insensitive zone pinball loss is related to the quantile distance therefore is not sensitive to noise, and the resulting solution is sparse. To improve the performance of TELM, we propose a novel TELM learning framework by introducing $\varepsilon $ -insensitive zone pinball loss function into TELM. Compared to TELM with hinge loss, the proposed SPTELM has the same computational complexity and is insensitive to noise, resampling stability and maintaining the sparsity of the solution. Further, we theoretically analyzed the sparsity, noise insensitivity and time complexity of SPTELM. Experimental results on multiple datasets demonstrate the noise insensitive, retains sparsity of the proposed method.
Highlights
Single-layer hidden layer feed-forward neural networks (SLFNs) [1] have received extensive attention and in-depth research in recent decades
Inspired and motivated by the studies of Twin extreme learning machine (TELM) and sparse pinball twin support vector machines (SPTSVM), in this paper, we propose a novel TELM learning framework based on ε-insensitive pinball loss called sparse twin extreme learning machine (SPTELM)
To further improve the generalization performance, we provide a novel TELM based on ε-insensitive pinball loss, which is called sparse twin extreme learning machine (SPTELM)
Summary
Single-layer hidden layer feed-forward neural networks (SLFNs) [1] have received extensive attention and in-depth research in recent decades. Ma: Sparse Twin Extreme Learning Machine With ε-Insensitive Zone Pinball Loss. The above pin-TSVM exhibits good performance for noise and resampling, it lacks sparsity To overcome this difficulty, Tanveer et al [36] proposed a novel sparse pinball twin support vector machines (SPTSVM) based on the ε-insensitive zone pinball loss function to rid the original TSVM of its noise insensitivity and ensure that the resulting TSVM problems retain sparsity which makes computations relating to predictions just as fast as the original TSVM. Inspired and motivated by the studies of TELM and SPTSVM, in this paper, we propose a novel TELM learning framework based on ε-insensitive pinball loss called sparse twin extreme learning machine (SPTELM).
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