Research on Forecasting Method Based on Genetic Algorithms and Support Vector Machines

Because hyperspectral remote sensing images have high band dimension and limited training samples, it is hard to achieve acceptable classification results using conventional statistical pattern recognition methods. Nevertheless, support vector machine (SVM) is suitable for hyperspectral image classification owing to its good generalization ability and minimal structural risk. Aimed to address the problems of large subjectivity, excessive time consumption, and low optimization accuracy in traditional SVM parameter selection, we applied genetic algorithm to optimize the model parameters. Using EO-1 Hyperion data as an example, an SVM object classification algorithm was proposed based on genetic algorithm optimization. To determine the effectiveness and superiority of this algorithm, a comprehensive evaluation and comparative analysis were performed with the classification effects of the cross-validation and maximum likelihood methods, respectively. Experimental results demonstrated that the proposed algorithm can effectively avert the aimlessness of artificial parameter selection and automatically optimize the SVM model parameters. It achieved relatively high overall classification accuracy (91.23%), which was 16.42% and 4.48% higher than the maximum likelihood method and cross-validation method, respectively.

This paper established evaluation index system of comprehensive utilization of land from the three aspects of economic benefits, ecological benefits and social benefits. Firstly, ridge regression (RR) is used to the indicator index sys- tem for screening, and then genetic algorithm and support vector machine (that is GASVM) is trained to the transformed samples. Moreover, the comprehensive evaluation results between the GASVM and ridge regression and genetic algo- rithm and support vector machine(that is RR-GASVM) is compared, which results by MATLAB show that using RR- GASVM is more accurate and higher efficient for the comprehensive utilization of land evaluation than that of GASVM. The combination of ridge regression and genetic algorithm and support vector machine can improve the decision ability of SVM, so that the decision has higher adaptability, which is the development direction of future research. At last, based on the results of this study, some suggestions are put forward accordingly. The area of Jiangsu, Zhejiang and Shanghai (that is Jiangzhehu) should consolidate the existing achievements and strengthen the innovation of the land management system on the basis of utilizing land comprehensively, realize the intensive, large-scale operation and speed up agricultural mod- ernization development by accelerating rural land circulation, and improve the efficiency of land utilization in order to save the land of urban construction. It is clear that people should insist the principle of paying equal attention to develop- ment and consolidation, and strengthen the cultivated land's ability to resist natural disasters in order to improve the eco- logical environment at the same time that improved the land utilization.

Considering the increasingly tense relationship between construction land supply and demand, we study the inherent rules and the spatial evolution in construction land use. In order to solve the problem of parameter optimization effectively, we analysis the fundamental theory of Support Vector Machine and finally accomplish the combination of genetic algorithm and support vector machine. Meanwhile we apply this model to analysis the construction land use and propose a new model, which is based on the support vector machines with genetic algorithm, for construction land evolution. Taking Guandu district in Kunming, Yunnan as a case, we find out that the new model is far superior to recent models in terms of predicting accuracy, algorithm complexity and computational efficiency. And therefore, we believe that this is highly precise, practical and efficient model for forecasting construction land use and evolution.

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset

Kernel selection is a key issue of support vector machine (SVM), however, for the existing criterions of kernel selection in the literature, there are some limitations in generalization ability , computational cost or fair comparison. This paper proposes a novel criterion of kernel selection for SVM named maximum robustness criterion. The properties of maximum robustness criterion guarantee that it has an excellent generalization ability. Moreover, the maximum robustness criterion is fair for different kernel functions because it is calculated in input space and has low computational cost. The experiment results on some benchmark data sets show that it not only achieves almost as good generalization ability as minimum misclassifying rate of k-fold cross validation, but also has less computational cost.

In many classification applications, Support Vector Machines (SVMs) have proven to be highly performing and easy to handle classifiers with very good generalization abilities. However, one drawback of the SVM is its rather high classification complexity which scales linearly with the number of Support Vectors (SVs). This is due to the fact that for the classification of one sample, the kernel function has to be evaluated for all SVs. To speed up classification, different approaches have been published, most which of try to reduce the number of SVs. In our work, which is especially suitable for very large datasets, we follow a different approach: as we showed in (Zapien et al. 2006), it is effectively possible to approximate large SVM problems by decomposing the original problem into linear subproblems, where each subproblem can be evaluated in Ω(1). This approach is especially successful, when the assumption holds that a large classification problem can be split into mainly easy and only a few hard subproblems. On standard benchmark datasets, this approach achieved great speedups while suffering only sightly in terms of classification accuracy and generalization ability. In this contribution, we extend the methods introduced in (Zapien et al. 2006) using not only linear, but also non-linear subproblems for the decomposition of the original problem which further increases the classification performance with only a little loss in terms of speed. An implementation of our method is available in (Ronneberger and et al.) Due to page limitations, we had to move some of theoretic details (e.g. proofs) and extensive experimental results to a technical report (Zapien et al. 2007).

Apoptosis is an essential process for controlling tissue homeostasis by regulating a physiological balance between cell proliferation and cell death. The subcellular locations of proteins performing the cell death are determined by mostly independent cellular mechanisms. The regular bioinformatics tools to predict the subcellular locations of such apoptotic proteins do often fail. This work proposes a model for the sorting of proteins that are involved in apoptosis, allowing us to both the prediction of their subcellular locations as well as the molecular properties that contributed to it. We report a novel hybrid Genetic Algorithm (GA)/Support Vector Machine (SVM) approach to predict apoptotic protein sequences using 119 sequence derived properties like frequency of amino acid groups, secondary structure, and physicochemical properties. GA is used for selecting a near-optimal subset of informative features that is most relevant for the classification. Jackknife cross-validation is applied to test the predictive capability of the proposed method on 317 apoptosis proteins. Our method achieved 85.80% accuracy using all 119 features and 89.91% accuracy for 25 features selected by GA. Our models were examined by a test dataset of 98 apoptosis proteins and obtained an overall accuracy of 90.34%. The results show that the proposed approach is promising; it is able to select small subsets of features and still improves the classification accuracy. Our model can contribute to the understanding of programmed cell death and drug discovery. The software and dataset are available at http://www.inb.uni-luebeck.de/tools-demos/apoptosis/GASVM.

Support vector machine is a machine learning method. It takes on the good generalization ability and prediction accuracy. But the parameters of SVM model seriously affect the generalization ability and prediction accuracy of SVM model on the great extent. So an improved particle swarm optimization (PSO) algorithm based on chaotic search is introduced into the SVM model in propose a novel data classification (AMPSVM) method for processing the complex data. The first, the ergodicity, stochastic property, and regularity of chaos is used to chaotically search the current best individual, which randomly replaces the selected individual in the population in order to speed up evolution, improve the searching ability, convergence speed and accuracy. Then the improved PSO algorithm is used to select and optimize the parameters of the SVM (AMPSVM) model in order to improve the learning performance and generalization ability of the SVM model. In order to verify the effectiveness of the AMPSVM method, UCI data is selected in here. The experiment results show that the proposed AMPSVM method takes on the strong generalization ability, best sensitivity and higher classification accuracy.

Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials

Hybrid algorithm for the classification of prostate cancer patients of the MCC-Spain study based on support vector machines and genetic algorithms

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires an accurate classification and recognition of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. This paper proposes a Genetic algorithm (GA) and Support Vector Machine (SVM) hybrid approach to accomplish this EEG classification task for potential BCI applications. An Oddball stimulus program and evoked event-related coherence program were designed to evaluate our method. The present study systematically evaluates the performance of the one channel pair event-related coherence feature set for EEG signal classification of auditory task. A GA approach for feature selection is presented which used to reduce the dimension of event-related coherence feature parameters. With the base classifiers of SVM, classification experiments are carried out upon real EEG recordings. Experimental results suggest the feasibilities of the new feature set, and we also derive some valuable conclusions on the performance of the EEG signal classification methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for achieving real-time BCI system based on evoked potential event-related coherence in the future.

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.

To increase the efficiency and precision of large‐scale road network traffic flow prediction, a genetic algorithm‐support vector machine (GA‐SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA‐SVM model and parallel GA‐SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA‐SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

SummarySoftware fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.