Genetic Algorithm-based Support Vector Machine Research Articles

Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Automatic prediction of coronary artery disease using differential evolution-based support vector machine

Coronary artery disease (CAD) is a common heart disease that causes the blockage of coronary arteries. To reduce fatality, an accurate diagnosis of this disease is very important. Angiography is one of the most trustworthy and conventional methods for CAD diagnosis however, it is risky, expensive, and time-consuming. Therefore in this study, we proposed a differential evolution-based support vector machine (SVM) for early and accurate detection of CAD. To improve the accuracy, different data preprocessing techniques such as one-hot encoding and normalization are also used with differential evolution for feature selection before performing classification. The proposed approach is benchmarked with the Z-Alizadeh Sani and Cleveland datasets against four state-of-the-art machine learning algorithms, and a highly cited genetic algorithm-based SVM (N2GC-nuSVM). The experimental results show that our proposed differential evolution-based SVM outperforms all the compared algorithms. The proposed method provides accuracies of 95±1% and 86.22% for predicting CAD on the benchmark datasets.

Lead Zirconate Titanate-based bolt looseness monitoring using multiscale singular spectrum entropy analysis and genetic algorithm-based support vector machine

The looseness monitoring of bolted joints is a significant issue to ensure structural integrity and safety in the industrial field. This paper proposes a novel approach to monitor bolt looseness based on piezoelectric active sensing. During the research, piezoelectric material is acted as an exciter to generate ultrasonic signals and a transducer is used to receive ultrasonic signals. In the process of signal processing, singular spectrum analysis (SSA) including phase reconstruction and principal component analysis is adopted to decompose the signal. Multiscale sample entropy (MSE) is employed to map the dynamic characteristics and regularity of the decomposed signals on multiple scales. The proposed strategy, named multiscale singular spectrum entropy analysis, refers to use MSE values of the new time series decomposed and reconstructed by SSA, to extract signal characteristics. Such a strategy can explore the underlying dynamical characteristics of a signal quantitatively in the reconstructed phase space. In our research work, SSA is employed to decompose the signals acquired by Lead Zirconate Titanate (PZT) to matrices, arranged from high to low singular values, and reconstruct the new time series (principal components) by diagonal averaging on determined matrices to characterize the essential dynamic characteristics of signals. MSE values of the principal components are used as damage index and adopted as input of genetic algorithm-based SVM to train a classifier to fulfill accurate monitoring of bolt joints. The theoretical derivation, application researches and comparison analysis can validate the effectiveness and superiority of the proposed approach in the field of bolt looseness monitoring.

A Genetic Algorithm-Based Support Vector Machine Approach for Intelligent Usability Assessment of m-Learning Applications

In the field of human computer interaction (HCI), the usability assessment of m-learning (mobile-learning) applications is a real challenge. Such assessment typically involves extraction of best features of an application like efficiency, effectiveness, learnability, cognition, memorability, etc., and further ranking of those features for overall assessment of the quality of the mobile application. In the previous literature, it is found that there is neither any theory nor any tool available to measure or assess a user’s perception and assessment of usability features of a m-learning application for the sake of ranking of the graphical user interface of a mobile application in terms of a user’s acceptance and satisfaction. In this paper, a novel approach is presented by performing a mobile application’s quantitative and qualitative analysis. Based on the user’s requirements and perception, a criterion is defined based on a set of important features. Afterwards, for the qualitative analysis, genetic algorithm (GA) is used to score prescribed features for usability assessment of a mobile application. The used approach assigns a score to each usability feature according to the user’s requirement and weight of each feature. GA performs the rank assessment process initially by performing feature selection and scoring the best features of the application. A comparison of assessment analysis of GA and various machine learning models, i.e., K-nearest neigbors, Naïve Bayes, and Random Forests is performed. It was found that GA-based support vector machine (SVM) provides more accuracy in the extraction of best features of a mobile application and further ranking of those features.

Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Maize (Zea mays subsp. mays) is a staple food crop in the world. Drought is one of the most common abiotic challenges that maize faces when it comes to growth, development, and production. Further knowledge of drought tolerance could aid with maize production. However, there has been less study focused on investigating in depth the drought tolerance of inbred maize lines using artificial intelligence techniques. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of the developed models was evaluated based on statistical indices and graphical representations. The results of the gamma test based on the least value of gamma and standard error indices show that days of anthesis (DOA), days of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined to be an efficient information vector combination for the drought-tolerance index (DTI) as well as the stress-tolerance index (STI). The MLP, SVM, MLP-GA, and SVM-GA algorithms’ results were compared based on statistical indices and visual interpretations that have satisfactorily predict the drought-tolerance index and stress-tolerance index in maize crops. The genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found to better predict the drought-tolerance index and stress-tolerance index in maize crops. Similarly, the SVM-GA model was found to have the highest potential to forecast the DTI and STI in maize crops, compared to the MLP, SVM, and MLP-GA models.

Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain

In this paper, a blockchain-based secure routing model is proposed for the Internet of Sensor Things (IoST). The blockchain is used to register the nodes and store the data packets’ transactions. Moreover, the Proof of Authority (PoA) consensus mechanism is used in the model to avoid the extra overhead incurred due to the use of Proof of Work (PoW) consensus mechanism. Furthermore, during routing of data packets, malicious nodes can exist in the IoST network, which eavesdrop the communication. Therefore, the Genetic Algorithm-based Support Vector Machine (GA-SVM) and Genetic Algorithm-based Decision Tree (GA-DT) models are proposed for malicious node detection. After the malicious node detection, the Dijkstra algorithm is used to find the optimal routing path in the network. The simulation results show the effectiveness of the proposed model. PoA is compared with PoW in terms of the transaction cost in which PoA has consumed 30% less cost than PoW. Furthermore, without Man In The Middle (MITM) attack, GA-SVM consumes 10% less energy than with MITM attack. Moreover, without any attack, GA-SVM consumes 30% less than grayhole attack and 60% less energy than mistreatment. The results of Decision Tree (DT), Support Vector Machine (SVM), GA-DT, and GA-SVM are compared in terms of accuracy and precision. The accuracy of DT, SVM, GA-DT, and GA-SVM is 88%, 93%, 96%, and 98%, respectively. The precision of DT, SVM, GA-DT, and GA-SVM is 100%, 92%, 94%, and 96%, respectively. In addition, the Dijkstra algorithm is compared with Bellman Ford algorithm. The shortest distances calculated by Dijkstra and Bellman are 8 and 11 hops long, respectively. Also, security analysis is performed to check the smart contract’s effectiveness against attacks. Moreover, we induced three attacks: grayhole attack, mistreatment attack, and MITM attack to check the resilience of our proposed system model.

A genetic algorithm-based support vector machine model for detection of hearing thresholds

ABSTRACT Auditory evoked potentials (AEPs), which are detected on the EEG auditory cortex area, are very small signals in response to a sound stimulus (or electric) from the inner ear to the cerebral cortex. These signals are recorded from electrodes attached to the scalp and are used for measuring the bioelectric function of the auditory pathway. In order to characterise their dynamic behaviour and due to the complex behaviour of nonlinear dynamic properties of EEG signals, several nonlinear analyses have been performed. In this work, Detrented Fluctuation Analysis (DFA) is applied to estimate the Fractal Dimension (FD) from the recorded AEP signals of the normal and the impaired hearing subjects. This aims at detecting their hearing threshold level. With the aim of classifying both groups, the normal and hearing impaired subjects, a hybrid approach based on the Support Vector machines (SVM) and genetic algorithms (GA) is taken: GA is applied to simultaneously optimise both SVM kernel parameters and feature subset selection. Our results indicate that the hybrid GA-SVM is promising; it is able to determine well the SVM kernel parameters along with feature subset selection, which will result in a high classification accuracy.

Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model.

Background Psoriasis is a chronic autoimmune disease impairing significantly the quality of life of the patient. The diagnosis of the disease is done via a visual inspection of the lesional skin by dermatologists. Classification of psoriasis using gene expression is an important issue for the early and effective treatment of the disease. Therefore, gene expression data and selection of suitable gene signatures are effective sources of information. Methods We aimed to develop a hybrid classifier for the diagnosis of psoriasis based on two machine learning models of the genetic algorithm and support vector machine (SVM). The method also conducts gene signature selection. A publically available gene expression dataset was used to test the model. Results A number of 181 probe sets were selected among the original 54,675 probes using the hybrid model with a prediction accuracy of 100% over the test set. A number of 10 hub genes were identified using the protein-protein interaction network. Nine out of 10 identified genes were found in significant modules. Conclusions The results showed that the genetic algorithm improved the SVM classifier performance significantly implying the ability of the proposed model in terms of detecting relevant gene expression signatures as the best features.

A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams

Abstract Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with previous models were discussed in TMC estimation by using a wide range of hydraulic and geometrical data from field and laboratory experiments. According to statistical analysis, the performance of the mentioned models in both straight and meandering streams was more accurate than the regression-based models. Sensitivity analysis showed that the accuracy of the GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters. Eliminating the uncorrelated parameters and reducing the number of input parameters will reduce the complexity of the problem and improve the TMC estimation by GA-SVM.

Application of genetic algorithm based support vector machine in selection of new EEG rhythms for drowsiness detection

The electroencephalogram (EEG) signals are important for drowsiness detection. However, in some specific application scenarios, whether there is a more accurate rhythm for drowsiness detection is worth further study. Therefore, a method of finding the optimal EEG rhythm for drowsiness detection using the genetic algorithm based support vector machine (GA-SVM) has been proposed in this study. This study used the original EEG signals in the Sleep EDF [Expanded] database for analysis and experiments. First, the original signals were divided into several epochs, and the signals of each epoch were decomposed using db10 wavelet packet transform and haar wavelet packet transform, respectively. Then, the GA-SVM was used to select the most accurate rhythm for drowsiness detection. Finally, leave-one-subject-out cross-validation (LOSO-CV) was used to evaluate the performance of each rhythm for drowsiness detection. The results show that the gamma rhythm has the best detection efficiency in the five traditional rhythms, and the accuracy rate is 80.94%. The detection accuracy of the new rhythm Rhythm (III) (43.75–48.046875 Hz) proposed in this study is 89.52%. The new rhythm proposed in this study showed bast performance in drowsiness detection.

Cultural Emperor Penguin Optimizer and Its Application for Face Recognition

Face recognition is an important technology with practical application prospect. One of the most popular classifiers for face recognition is support vector machine (SVM). However, selection of penalty parameter and kernel parameter determines the performance of SVM, which is the major challenge for SVM to solve classification problems. In this paper, with a view to obtaining the optimal SVM model for face recognition, a new hybrid intelligent algorithm is proposed for multiparameter optimization problem of SVM, which is a fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), namely, cultural emperor penguin optimizer (CEPO). The key aim of CEPO is to enhance the exploitation capability of EPO with the help of cultural algorithm basic framework. The performance of CEPO is evaluated by six well-known benchmark test functions compared with eight state-of-the-art algorithms. To verify the performance of CEPO-SVM, particle swarm optimization-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), CA-SVM, and EPO-SVM, moth-flame optimization-based SVM (MFO-SVM), grey wolf optimizer-based SVM (GWO-SVM), cultural firework algorithm-based SVM (CFA-SVM), and emperor penguin and social engineering optimizer-based SVM (EPSEO-SVM) are used for the comparison experiments. The experimental results confirm that the parameters optimized by CEPO are more instructive to make the classification performance of SVM better in terms of accuracy, convergence rate, stability, robustness, and run time.

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.

Gastric cancer (GC) is a heterogeneous tumour with numerous differences of epidemiologic and clinicopathologic features between cardia cancer and non‐cardia cancer. However, few studies were performed to construct site‐specific GC prognostic models. In this study, we identified site‐specific GC transcriptomic prognostic biomarkers using genetic algorithm (GA)‐based support vector machine (GA‐SVM) and GA‐based Cox regression method (GA‐Cox) in the Cancer Genome Atlas (TCGA) database. The area under time‐dependent receive operating characteristic (ROC) curve (AUC) regarding 5‐year survival and concordance index (C‐index) was used to evaluate the predictive ability of Cox regression models. Finally, we identified 10 and 13 prognostic biomarkers for cardia cancer and non‐cardia cancer, respectively. Compared to traditional models, the addition of these site‐specific biomarkers could notably improve the model preference (cardia: AUCtraditional vs AUCcombined = 0.720 vs 0.899, P = 8.75E‐08; non‐cardia: AUCtraditional vs AUCcombined = 0.798 vs 0.994, P = 7.11E‐16). The combined nomograms exhibited superior performance in cardia and non‐cardia GC survival prediction (C‐indexcardia = 0.816; C‐indexnoncardia = 0.812). We also constructed a user‐friendly GC site‐specific molecular system (GC‐SMS, https://njmu‐zhanglab.shinyapps.io/gc_sms/), which is freely available for users. In conclusion, we developed site‐specific GC prognostic models for predicting cardia cancer and non‐cardia cancer survival, providing more support for the individualized therapy of GC patients.

Rapid Screening of Thyroid Dysfunction Using Raman Spectroscopy Combined with an Improved Support Vector Machine.

This study aimed to screen for thyroid dysfunction using Raman spectroscopy combined with an improved support vector machine (SVM). In spectral analysis, in order to further improve the classification accuracy of the SVM algorithm model, a genetic particle swarm optimization algorithm based on partial least squares is proposed to optimize support vector machine (PLS-GAPSO-SVM). In order to evaluate the performance of the algorithm, five optimization algorithms are used: grid search-based SVM (Grid-SVM), particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), artificial fish coupled uniform design algorithm-based SVM (AFUD-SVM), and simulated annealing particle swarm optimization algorithm-based SVM (SAPSO-SVM). In this experiment, serum samples from 95 patients with confirmed thyroid dysfunction and 90 serum samples from normal thyroid function were used for Raman spectroscopy. The experimental results show that the GAPSO-SVM algorithm has a high average diagnostic accuracy of 95.08% and has high sensitivity and specificity (91.67%, 97.96%). Compared with the traditional optimization algorithm, the algorithm has high diagnostic accuracy, short execution time, and good reliability. It can be seen that Raman spectroscopy combined with GAPSO-SVM diagnostic algorithm has enormous potential in noninvasive screening of thyroid dysfunction.

Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods-permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)-as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The 'coordinate value' in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness.

Stability analysis of GMAW based on multi-scale entropy and genetic optimized support vector machine

The gas metal arc welding is a complex chaotic dynamic process. To study the relationship between arc electrical signal and welding stability, the multi-scale entropy method was introduced to analyze the current signals under different welding process parameters. Under the short-circuiting droplet transition mode, the larger shielding gas flow rate led to, the more stable welding and the smaller amplitude of multi-scale entropy curves. When the welding current parameter increased gradually, the droplet transition mode changed, and the amplitude of multi-scale entropy curves increased. As the welding voltage rose, the droplet transfer frequency decreased and the multi-scale entropy increased. Furthermore, the four-class prediction of welding forming quality was studied by combining with the genetic algorithm-based support vector machine (GA-SVM). The multi-scale entropy distribution was closely related to the type and stability of short-circuiting transfer in the welding process.

A memetic algorithm using emperor penguin and social engineering optimization for medical data classification

Gene selection and classification of microarray data play an important role in cancer diagnosis and treatment. One of the most popular and faster classification model is support vector machine (SVM). However, the major challenge in SVM lies in the selection of its two parameters, namely, regularization parameter C and kernel parameter γ. Attempts have been made to improve the performance of SVM by tuning these two parameters with the help of metaheuristics. Although existing metaheuristics can search the promising regions of the search space, they are unable to explore the global optimum efficiently. In this paper, a memetic algorithm-based SVM (M-SVM) is presented for simultaneous feature selection and optimization of SVM parameters. The memetic algorithm is a fusion of local search strategy using social engineering optimizer (SEO) and global optimization framework using emperor penguin optimizer (EPO). The idea of embedding SEO in EPO is to enhance the exploitation capability of EPO. The performance of our algorithm is evaluated on seven standard benchmark datasets. To prove the efficacy of the method, it is compared with particle swarm optimization based SVM (PSO-SVM), genetic algorithm based SVM (GA-SVM), and fifteen other state-of-the-art methods. The experimental results confirm that the proposed method significantly outperforms other existing techniques in terms of accuracy and number of selected genes. The proposed method is validated using a statistical analysis, namely, ANOVA.

Short-term PV power forecasting using hybrid GASVM technique

The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error.

An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel ${{\rm{K}}_{{\mathrm{DDC}}}}$ formed by commuting the cross correlation kernel ${{\rm{K}}_{{\mathrm{xcorr}},\mathrm{ij}}}$ and the convolution kernel ${{\rm{K}}_{{\mathrm{conv}},\mathrm{ij}}}$ . ${{\rm{K}}_{{\mathrm{xcorr}},\mathrm{ij}}}$ , and captures the symmetric information among ECG signals from different classes, while ${{\rm{K}}_{{\mathrm{conv}},\mathrm{ij}}}$ captures the antisymmetric information among ECG signals from the same class. The final ${{\rm{K}}_{{\mathrm{DDC}}}}$ (a precomputed kernel) is obtained by a genetic mutation using a multi-objective genetic algorithm. This renders an optimal ${{\rm{K}}_{{\mathrm{DDC}}}}$ that confidently serves as the full descriptor of the drowsiness. The performance of ${{\rm{K}}_{{\mathrm{DDC}}}}$ is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of ${{\rm{K}}_{{\mathrm{DDC}}}}$ is better than those of both ${{\rm{K}}_{{\mathrm{xcorr}},\mathrm{ij}}}$ and ${{\rm{K}}_{{\mathrm{conv}},\mathrm{ij}}}$ by more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17–63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, K DDC improves the accuracy by 48.4–87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

Genetic algorithm based support vector machine for on-line voltage stability monitoring

Abstract A Genetic Algorithm based Support Vector Machine (GA-SVM) approach for online monitoring of long-term voltage instability has been proposed in this paper. The conventional methods for voltage stability monitoring are highly time consuming hence infeasible for online application. Support vector machine is a powerful and promising function estimation tool. To improve the accuracy and minimize the training time of SVM, the optimal values of SVM parameters are obtained using genetic algorithm. The proposed approach uses the voltage magnitude and phase angle obtained from Phasor Measurement Units (PMUs) as the input vectors to SVM and the output vector is the Voltage Stability Margin Index (VSMI). The effectiveness of the proposed approach is tested using the New England 39-bus test system and the Indian Northern Region Power Grid (NRPG) 246-bus real system. The results of the proposed GA-SVM approach for voltage stability monitoring are compared with grid search SVM (GS-SVM) and artificial neural networks (ANN) approach with same data set to prove its superiority.

Support Vector Machine (SVM) Based on Membrane Computing Optimization and the Application for C-band Radio Abnormal Signal Identification

The Support Vector Machine (SVM) is a widely used tool in classification problems, but the classification performance of Support Vector Machine (SVM) largely depends on the choice of its relevant parameters. This paper proposes a model of Support Vector Machine (SVM) classification based on Cell-like Membrane computing Optimization algorithm (CMO-SVM). In the model, the parameters of Support Vector Machine (SVM) (cost parameter C and RBF kernel parameter ) are optimized by cell-like membrane computing optimization algorithm for the sake of getting the best combination parameters of SVM for classification. This method overcomes the insufficiency of the conventional method which converged to local optimum, at the same time also has the advantages of good robustness, fast convergence speed and obtains the global optimal solution. Finally, to show the applicability and superiority of the proposed algorithm, the method is employed to identify abnormal signal of c-band radio (including radar, jammer, single carrier and single frequency point). Compared with Genetic Algorithm-based SVM (GA-SVM), Simulated Annealing algorithm-based SVM (SA-SVM), Ant Colony algorithm-based SVM (AC-SVM), the proposed model performs best for the four abnormal signal.