Multiple Kernel Support Vector Regression Research Articles

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

Automatic optimized support vector regression for financial data prediction

The aim of this article is to introduce a hybrid approach, namely optimal multiple kernel–support vector regression (OMK–SVR) for time series data prediction and to analyze and compare its performances against those of support vector regression with a single RBF kernel (RBF-SVR), gene expression programming (GEP) and extreme learning machine (ELM) on the financial series formed by the monthly and weekly values of Bursa Malaysia KLCI Index, monthly values of Dow Jones Industrial Average Index (DJIA) and New York Stock Exchange. Our method provides an optimal multiple kernel and optimal parameters in Support Vector Regression, improving the accuracy of prediction. The proposed approach is structured on two levels. The macro-level uses a breeder genetic algorithm for choosing the optimal multiple kernel and the SVR optimal parameters. The fitness function of each chromosome is computed in the micro-level using a SVR algorithm. The regression model based on the optimal multiple kernel and optimal parameters is then validated and used for forecasting. The experimental results prove that OMK–SVR performs better than GEP, RBF-SVR and ELM for predicting the future behavior of the study series. A sensitivity study with respect to the number of kernels from the multiple kernel used by OMK–SVR and with respect to the ratio between training and testing data sets was conducted.

Synergistic Use of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for Urban Impervious Surface Mapping

Accurate mapping of impervious surface distribution is important but challenging. Integrating optical and SAR data to improve urban impervious surface estimation has recently shown promising performance. Further investigation and development on this multisensory approach are conducted in this study. A novel multiple kernel learning (MKL) framework is proposed to integrate heterogeneous features from Landsat-8 and Sentinel-1A data effectively. A linearly weighted combination of basic kernels built using each group of features is learned as the optimal kernel, while the hyperparameters and the weight of each basic kernel are determined simultaneously by using the differential evolution algorithm. Then, the optimal kernel is embedded into the support vector regression algorithm, and the impervious surface abundance of the study area is estimated by applying the developed multiple kernel support vector regression (MKSVR) model. The impervious surface ground truth at a subpixel level is derived from a high-resolution image by means of object-oriented classification. The experimental results indicate that the synergistic use of optical and dual-pol SAR data by employing MKSVR achieves a noteworthy improvement for impervious surface estimation compared to that using optical image alone, the root mean square error is decreased by 4.30%, and the coefficient of determination ( R 2) is increased by 9.47%, and that the incorporation of optical and SAR does not guarantee the improved performance, simply stacking all features of multisource data into a vector is not a good choice, and the MKL is a powerful tool to apply as demonstrated by the experiments conducted in this study.

Speed estimation of traffic flow using multiple kernel support vector regression

Industrial loop detectors (ILDs) are the most common traffic detectors. In Shanghai, most of the ILDs are installed in a single loop way, which can detect various parameters, such as flow, saturation, and so on. However, they cannot detect the speed directly, which is one of the key inputs of intelligent transportation systems (ITS) for identifying the traffic state. Thus, this paper is dedicated to estimate speed accurately. It proposes a new algorithm that multiple kernel support vector regression (MKL-SVR) to complete this goal, which improves the accuracy and robustness of the speed estimation. Extensive experiments have been performed to evaluate the performances of MKL-SVR, compared with polynomial fitting, BP neural networks and SVR. All results indicate that the performances of MKL-SVR are the best and most robust.

Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data

The appropriate spectral vegetation indices can be used to rapidly and non-destructively estimate the leaf nitrogen concentration (LNC) in wheat for on-farm wheat management. However, the accuracy of estimation should be further improved. Previous studies focused on developing vegetation indices, but research about modeling algorithms were limited. In this study, multiple-kernel support vector regression (MK-SVR) was used to assess the LNC in wheat based on satellite remote sensing data. The objectives of this study were to (1) investigate the applicability of the MK-SVR algorithm for remotely estimating the LNC in wheat, (2) test the performance of the MK-SVR regression model, and (3) compare the performance of the MK-SVR algorithm with multiple linear regression (MLR), partial least squares (PLS), artificial neural networks (ANNs), and single-kernel SVR (SK-SVR) algorithms for wheat LNC estimation. In-situ LNC data over four years at different sites in Jiangsu Province of China were measured during the jointing, booting, and anthesis stages; one HJ-CCD image of wheat was obtained during each stage. Vegetation indices were calculated based on these images, and correlations between vegetation indices and LNC data were measured. Finally, a MK-SVR model whose inputs were vegetation indices was established to estimate the LNC during each stage. The results showed that the MK-SVR model performed well in estimating LNC. The coefficients of determination (R2) of the estimated-versus-measured LNC values for the three stages were respectively 0.73, 0.82, and 0.75, meanwhile, the corresponding root mean square errors (RMSE) and the relative RMSE were respectively 0.13 and 6.6%, 0.21 and 7.7%, and 0.20 and 6.5%. Thus, the MK-SVR algorithm provides an effective way to improve the prediction accuracy of LNC in wheat on a large scale.

Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth

There are two main issues of concern for land change scientists to consider. First, selecting appropriate and independent land cover change (LCC) drivers is a substantial challenge because these drivers usually correlate with each other. For this reason, we used a well-known machine learning tool called genetic algorithm (GA) to select the optimum LCC drivers. In addition, using the best or most appropriate LCC model is critical since some of them are limited to a specific function, to discover non-linear patterns within land use data. In this study, a support vector regression (SVR) was implemented to model LCC as SVRs use various linear and non-linear kernels to better identify non-linear patterns within land use data. With such an approach, choosing the appropriate kernels to model LCC is critical because SVR kernels have a direct impact on the accuracy of the model. Therefore, various linear and non-linear kernels, including radial basis function (RBF), sigmoid (SIG), polynomial (PL) and linear (LN) kernels, were used across two phases: 1) in combination with GA, and 2) without GA present. The simulated maps resulting from each combination were evaluated using a recently modified version of the receiver operating characteristics (ROC) tool called the total operating characteristic (TOC) tool. The proposed approach was applied to simulate urban growth in Rasht County, which is located in the north of Iran. As a result, an SVR-GA-RBF model achieved the highest area under curve (AUC) value at 94% while the lowest AUC was achieved when using the SVR-LN model at 71%. The results show that the synergy between GA and SVR can effectively optimize the variables selection process used when developing an LCC model, and can enhance the predictive accuracy of SVR.

Multiple kernel support vector regression for pricing nifty option

<p>The goal of present experiments is to investigate the use of multiple kernel learning as a tool for pricing options in the context of Indian stock market for Nifty index options. In this paper, fair price of an option is predicted by Multiple Kernel Support Vector Regression (MKLSVR) using linear combinations of kernels and Single Kernel Support Vector Regression (SKSVR). Prices of option highly depend on different money market conditions like deep-in-the-money, in-the-money, at-the-money, out-of-money and deep-out-of-money condition. The experimental study attempts to identify the forecasting errors with the help of mean square error; root meant square error, and normalized root meant square error between the market option prices and the calculated option prices by model for all market conditions. The results reflect that multiple kernel support vector regression performed fairly well in comparison to support vector regression with single kernel.</p>

A Fast Luminance Inspector for Backlight Modules Based on Multiple Kernel Support Vector Regression

The liquid crystal display (LCD) is widely used in various devices nowadays, for examples, mobile phones, digital cameras, and machine controllers. Among the components composing an LCD, the backlight module is one of the most important components because it provides a uniform light source for the display. In the current practice of backlight module inspection, a luminance colorimeter is used to examine the luminance emitted from selected areas called check points within the backlight module. Since a backlight module consists of many check points, it takes too much time to inspect one backlight module in the current practice. In this paper, we propose a fast luminance inspector that uses a CCD camera to inspect multiple backlight modules, simultaneously. The inspection speed is improved considerably, and thus, makes the proposed system suitable for use in the production line. The proposed system translates image intensities of all check points into luminance values based on a multiple kernel support vector regression model (MKSVR). The parameters of multiple kernel functions are automatically generated, according to the data distribution characteristics of the training samples. Compared with other studies using a grid search or a heuristic search algorithm to determine optimal kernel parameters, the proposed approach is more flexible and faster. Besides that, as the kernel parameters are adaptive to the training data, the proposed method could make its learning more specific to certain golden training samples. Other methods using learning algorithms such as the neural network and the SVR do not have such flexibility, because in these learning algorithms all training samples are treated equally. The flexible learning property makes the proposed system more appealing for use in the real practical world. Compared with other methods using the neural network model and the SVR model in backlight module inspection, the proposed method is superior in both the accuracy and the training time required.

Dynamic customer lifetime value prediction using longitudinal data: An improved multiple kernel SVR approach

Customer lifetime value (CLV), as an important metric in customer relationship management (CRM), has attracted widespread attention over the last decade. Most CLV prediction models do not take into consideration the dynamics of the customer purchase behavior and changes of the marketing environment such as the adoption of different promotion policies. In this study, a framework for the dynamic CLV prediction using longitudinal data is presented. In the framework, both the dynamic customer purchase behavior and customized promotions are considered. An improved multiple kernel support vector regression (MK-SVR) approach is developed to predict the future CLV and select the best promotion using both the customer behavioral variables and controlled variable about multiple promotions. Computational experiments using two databases show that the MK-SVR exhibits good prediction performance and the usage of longitudinal data in the MK-SVR facilitate the dynamic prediction and promotion optimization.

ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model.

A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction

The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.