A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell

At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfactory data collection is relatively cumbersome, and it is more difficult to collect labeled data. However, in many scenarios, to use a small amount of labeled data to train a good-performing classifier, it is not feasible to rely only on supervised learning algorithms, but semi-supervised learning algorithms can better cope with only a small amount of labeled data and a large amount of unlabeled data. This study combines the new weighted kernel with SKELM and proposes a semi-supervised extreme learning machine algorithm based on the weighted kernel, SELMWK. The experimental results show that the proposed SELMWK algorithm has good classification performance and can solve the semi-supervised gas classification task of the same domain data well on the used dataset.

Automated summary

Machine sense of smell plays an important role in various scenarios, but it relies on data and algorithms for support. This study proposes a semi-supervised extreme learning machine algorithm, SELMWK, which combines weighted kernel with SKELM to handle a small amount of labeled data and a large amount of unlabeled data, achieving good classification performance.