Abstract
In many fields, such as oil logging, it is expensive to obtain labeled data, and a large amount of inexpensive unlabeled data are not used. Therefore, it is necessary to use semisupervised learning to obtain accurate classification with limited labeled data and many unlabeled data. The semisupervised support vector machine (S3VM) is the most useful method in semisupervised learning. Nevertheless, S3VM model performance will degrade when the sample number of categories is not even or have lots of unlabeled samples. Thus, a new semisupervised SVM by hybrid whale optimization algorithm (HWOA-S3VM) is proposed in this paper. Firstly, a tradeoff control parameter is added in S3VM to deal with an uneven sample of category which can cause S3VM to degrade. Then, a hybrid whale optimization algorithm (HWOA) is used to optimize the model parameters of S3VM to increase the classification accuracy. For HWOA improvement, an opposition-based cubic mapping is used to initialize the WOA population to improve the convergence speed, and the catfish effect is used to help WOA jump out of the local optimum and obtain the global optimization ability. In the experiments, firstly, the HWOA is tested by 12 classic benchmark functions of CEC2005 and four functions of CEC2014 compared with the other five algorithms. Then, six UCI datasets are used to test the performance of HWOA-S3VM and compared with the other four algorithms. Finally, we applied HWOA-S3VM to perform oil layer recognition of three oil well datasets. These experimental results show that (1) HWOA has a higher convergence speed and better global searchability than other algorithms. (2) HWOA-S3VM model has higher classification accuracy on UCI datasets than other algorithms when combined, labeled, and unlabeled data are used as the training dataset. (3) The recognition accuracy and speed of the HWOA-S3VM model are superior to the other four algorithms when applied in oil layer recognition.
Highlights
Semisupervised learning (SSL) [1] is between supervised learning and unsupervised learning, which is taking advantage of both learning methods while using labeled samples to learn and those unmarked samples
(2) hybrid whale optimization algorithm (HWOA)-S3VM model has higher classification accuracy on UCI datasets than other algorithms when combined, labeled, and unlabeled data are used as the training dataset. (3) e recognition accuracy and speed of the HWOA-S3VM model are superior to the other four algorithms when applied in oil layer recognition
We propose an improved S3VM, firstly, adding a tradeoff control parameter in S3VM to deal with an uneven sample of category which can cause S3VM to degrade. en, using a hybrid whale optimization algorithm (HWOA) to optimize the model parameters of S3VM, the HWOA is mainly improved in two aspects; on the one hand, the chaotic population method is used to initialize the whale population to improve the algorithm’s local optimization ability; on the other hand, the population is improved by the catfish effect to improve the algorithm’s overall optimization ability
Summary
Semisupervised learning (SSL) [1] is between supervised learning and unsupervised learning, which is taking advantage of both learning methods while using labeled samples to learn and those unmarked samples. Mohammed et al [33] combined bat and WOA algorithm; the test result shows the WOA-BAT algorithm is competitive than WOA in multimodal functions These algorithms need to be further improved in convergence speed and accuracy. In literature [31], they have achieved good results in population evolution with chaotic-mapping for WOA, so we try to combine opposition-based learning and chaoticmapping for population initialization to enhance the WOA population’s local optimization ability. The new S3VM model based on the improved WOA algorithm is applied in oil layer recognition.
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