Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition

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.

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

“…It is noted that the task of searching for those hyperparameters can be considered as a global optimization problem [28,32,[64][65][66][67][68][69][70][71]. Moreover, since C and σ are searched in continuous space, the number of parameter combinations is infinitely large.…”

“…In addition, although machine learning methods have been extensively used in computer vision-based structural health monitoring [3,12,[24][25][26], hybrid approaches that combine the strengths of machine learning and metaheuristic algorithms are rarely investigated in this field especially for concrete spall recognition. Metaheuristic algorithms can be used to optimize the learning phase of machine learning models and therefore help to achieve better predictive performances [27][28][29][30][31][32][33].…”

A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

“…If3 X gbest did not evolve in five iterations, then rank the search agent fitness from best to worst, and initialize the position of 10% of the worst search agents with Equation ( 16) end if3 end for t = t + 1 end while return X gbest In order to verify the effectiveness and generalization of the improved algorithm, we compare the IWOA with WOA [14], PSO [11], GWO [12], HWOA [31] and MPA [13] on 15 benchmark functions [37], where F1-F5 are unimodal benchmark functions. From F6 to F10 are multimodal benchmark functions, and F11 to F15 are fixed-dimension multimodal benchmark functions.…”

“…Those four classic algorithms are PSO, GWO, WOA, MPA. The improved whale optimization algorithm is HWOA [31]. For all of the experiment data, we use a box line diagram and the Wilcoxon Rank Sum Test to test the stability and competitiveness of IWOA.…”

An Improved Equilibrium Optimizer Algorithm and Its Application in LSTM Neural Network

“…They all have excellent performances in combinatorial optimization [5], feature selection [6], image processing [7], data mining [8], and many other fields. In recent years, new meta-heuristic algorithms based on mimic the natural behavior have been proposed one after another, including particle swarm optimization (PSO) [9,10], gray wolf optimization (GWO) [11], seagull optimization algorithm (SOA) [12], whale optimization algorithm (WOA) [13,14], cuckoo search algorithm (CSA) [15,16], marine predator algorithm (MPA) [17,18], coyote optimization algorithm (COA) [19], carnivorous plant algorithm (CPA) [20], transient search algorithm (TSA) [21], genetic algorithms (GA) [22] and more.…”