Research On Meta-heuristic Optimized Extreme Learning Machine Based Classification Algorithms And Application

The Classification problem is one of the most important research topics in the areas ofpattern recognition, data mining and machine learning. The neural networks methods as animportant branch of topic, especially, the proposed Back Propagation (BP) algorithmpromotes the rapid development of neural networks in the fields of theoretical research andtechnology application. Neural networks methods can effectively find the information andpatterns from the data by independent learning, which provide an effective way to solve theseclassification problems. However, traditional neural networks such as BP algorithm andSupport Vector Networks both cost much training time, converge with a slow speed and trapin local optimum easily, how to design a classification method with validity and goodgeneralization ability, and to provide support for the scientific research and technicalapplication, that is a difficult problem need to be well solved.This paper focuses on a novel classification method called Extreme Learning Machine(ELM), which has been proposed and studied recently in the designing and construction ofclassification methods with effectiveness and good generaliza tion ability. We have proposedArtificial Bee Colony (ABC) algorithm based ELM classification method. The self-adaptiveABC method based multi-kernel ELM classification method. The local fisher discriminateanalysis method based improved kernelized ELM system for thyroid disease diagnosis model.The adaptive subtract clustering feature weighting (ASCFW) method based kernelized ELMmodel for Parkinson’s disease diagnosis model. The improved gravitational search algorithmbased kernelized ELM method for classification problems.The contributions and innovations of this paper are briefly given as follows:(1) We make a brief discussion on theoretical methods of neural networks, and make ananalysis and discussion on the development trend and defect of these methods. Moreover,we analyze and discuss the classification principle, the research status and the defect ofELM methods in detail. This part has laid the foundation for the next research.(2) ABC-based ELM method for classificationThe performance of ELM algorithm depends on the input weights and biases values of neural networks, so we use ABC algorithm to optimize the ELM parameters includinginput weights and biases. The superior of ABC algorithm with the global search abilitycan conduct ELM model to train and test, and considering the minimized output weightnorm in maximizing the classification accuracy at the same time. Experiment results showthat the proposed method can effectively improve the generalization performance of ELM,and the structure of network is more compact. The performance of kernel ELM is alsoaffected by different types of kernel functions, we propose the self-adaptive ABCalgorithm to optimize the multi-kernel ELM model. In this method, multiple kernel designcan be more flexible to reflect different data structures, SABC algorithm with four searchstrategies is used to adaptively optimize the related parameters of multi-kernel ELM forclassification. The experimental results show that the proposed method can achieve bettergeneralization performance than existing and similar methods in sixteen classificationdatasets, the construction of kernel function is flexible and reasonable, and the obtainedresults are more stable.(3) Feature extraction based improved kernel ELM model for Thyroid diagnosisThe related parameters of kernel ELM are important factors for improving theperformance of ELM methods. We propose a novel system that combines LFDA andimproved kernel ELM algorithm for Thyroid disease diagnosis. In this model, firstly,LFDA algorithm is used to achieve the best features subset and reduce the complexity ofmodel training. Secondly, the improved ABC algorithm is used to optimize the relatedparameters of kernel ELM model. At last, the well-trained model is used to calculate thepredict results. The experimental results confirm that the valid of the proposed model, andthis hybrid model can not only reduce the features dimension, but also enhance theclassification accuracy. It can be seen that the diagnosis accuracy rate of model is betterthan the existing methods in the area of Thyroid disease diagnosis.(4) Clustering algorithm based kernel ELM model for Parkinson’s disease diagnosisThe redundant and irrelevant features in original feature space can decrease theclassification performance of ELM method. To solve this issue, we combine the optimizdsubtract clustering feature weighting algorithm (ASCFW) with kernel ELM model forParkinson’s disease diagnosis. In this model, ASCFW algorithm is used to transform thefeatures space into linear separable space, differentiate the data that belongs to differentcategories. In addition, ABC algorithm is used adaptively to specify the neighborhoodradius and related parameters of SCFW algorithm, finding the closest cluster centers ofreal data distribution. The experimental results show that the proposed method can not only achieve significantly higher results than the existing methods in terms ofclassification accuracy rates, sensitivity, specificity, computation time and so on, but alsocan make effective diagnosis ofParkinson’s disease.(5) Improved gravitational search algorithm based kernel ELM method for classificationThe related parameters of kernel ELM model and feature selection both can affect theperformance of ELM, however, parameters optimization method and feature selection forELM always perform separately. To solve this problem, we propose a novel adaptiveclassification method based on improve GSA algorithm, IGSA algorithm combines withpattern search to improve the local search ability and convergence speed, and introducesdiversity factor to expand the search space. The discrete and continuous IGSA algorithmsare integrated into a unified algorithm framework, where IGSA is used to optimizefeatures subsets selection and parameters simultaneously. The obtained results show thatthis model can select the most related and important features of credit risk assessment, andthe classification rates are significantly better than the existing and similar methods.