Stepwise Support Vector Machine Research Articles

Proximally sensed digital data library to predict topsoil clay across multiple sugarcane fields of Australia: Applicability of local and universal support vector machine

Knowledge about topsoil (0–0.3 m) clay is required to maintain sugarcane profitability in Queensland, Australia. However, laboratory analysis to get this knowledge is tedious and time consuming. To add value to limited clay data, a digital soil map (DSM) can be created by using digital data and mathematical models. At the field level, site-specific linear regression (LR) models are often used along with gamma-ray (γ-ray) spectrometry and electromagnetic induction data (i.e. soil apparent electrical conductivity – ECa). But these LR might not perform well in site-independent calibrations across multiple sites. In this regard, support vector machine (SVM) might be useful. In this research, we first aimed to determine, using a stepwise SVM and calibration dataset, the optimal digital data (i.e. individual or combined) to develop local (for each individual site) and universal (for combined sites) SVM models. Using optimal digital data, our second aim was to predict clay for validation datasets by using local SVM in site-specific approach and universal SVM in site-independent, holdout and spiking approaches. Using these approaches, DSM of predicted clay and associated uncertainty were generated for a representative study site (i.e., Mossman). The third aim was to determine the suitable number of spiking samples and by varying the size of both spiking set and calibration model. Approaches were compared using prediction agreement (Lin’s concordance) and accuracy (ratio of performance to deviation – RPD). We concluded from stepwise SVM that combining digital data resulted in better accuracy (RPD = 2.17) than individual γ-ray (1.79) or ECa (1.49) data. In terms of independent validation, the results of Mossman reflected the general rank order of different approaches with site-specific (3.03) excellent, spiking (1.89) very good, site-independent (1.84) good and holdout (1.18) poor predictions. In case of DSM and uncertainty maps, under-predictions were problematic at field edges and where digital data changed as a function of soil type and or proximity to a prior stream channel. Considering the suitable number of spiking samples, size of spiking set showed linear relationship with improvement in predictions while that of calibration model showed no influence. These results suggest the usefulness of proximally sensed data and universal SVM to effectively predict clay across multiple sites.

A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM).

An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine learning has been broadly adopted for data analysis. In particular, the Support Vector Machine (SVM) has an excellent performance in classifications and predictions with the high-dimensional data. In this research, a novel model selection strategy is carried out, named as the Stepwise Support Vector Machine (StepSVM). The new strategy is based on the SVM to conduct a modified stepwise selection, where the tuning parameter could be determined by 10-fold cross-validation that minimizes the mean squared error. Two popular methods, the conventional stepwise logistic regression model and the SVM Recursive Feature Elimination (SVM-RFE), were compared to the StepSVM. The Stability and accuracy of the three strategies were evaluated by simulation studies with a complex hierarchical structure. Up to five variables were selected to predict the dichotomous cancer remission of a lung cancer patient. Regarding the stepwise logistic regression, the mean of the C-statistic was 69.19%. The overall accuracy of the SVM-RFE was estimated at 70.62%. In contrast, the StepSVM provided the highest prediction accuracy of 80.57%. Although the StepSVM is more time consuming, it is more consistent and outperforms the other two methods.

Feature analysis of cell nuclear chromatin distribution in support of cervical cytology

.Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist’s experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.

Step-wise support vector machines for classification of overlapping samples

Among machine learning algorithms, Support Vector Machine (SVM) is outstanding for its high efficiency and good generalization ability. This paper mainly concerns the classification performance of SVMs for multiple classes and auxiliary algorithms combined with SVMs. These auxiliary algorithms include Recursive Feature Elimination (RFE) algorithm, parameters optimizing methods and Two-Step Classification strategy. Results are given under data-based framework that classification ability and operation efficiency of SVMs are both improved when dimension is reduced; and Two-Step Classification SVM (TSC-SVM) works well under circumstances that samples overlap with each other seriously. In TSC-SVM, differences between adjacent samples are denoised by wavelet transform and magnified by a proper weighting function, after samples are sorted into correct groups in the first step. Discussions and comparisons are based on abalone dataset. According to the simulations, it is believed that step-wise SVMs have superior classification ability.

A novel stepwise support vector machine (SVM) method based on optimal feature combination for predicting miRNA precursors

MicroRNAs (miRNAs) are a class of non-coding RNAs that are produced from miRNA precursors (premiRNAs) with stem-loop structure. At present, development of computational approach for pre-miRNA identification continues to be a challenging task, in which feature selection is greatly important. Here, we first extracted feature subsets by a hybrid algorithm of genetic algorithm (GA) and support vector machine (SVM) from 124 sequence and secondary structure features. Next, based on the highfrequency features taken from the feature subsets, we proposed a novel stepwise SVM method to identify the optimal feature combinations. The cooperative effect was found among different features in our study. Finally, we obtained 10 feature combinations with strong combined effect which possessed high classification performance for predicting pre-miRNAs. In external validation, all the 10 combinations could predict accurately over 13 pre-miRNAs from 16 new confirmed human pre-miRNAs in miRBase 14.0. The best one could reach 15 (93.75%), which significantly outperformed triplet-SVM (13, 81.25%) in predicting pre-miRNAs.

Recovery Discrimination based on Optimized-Variables Support Vector Machine for Nonperforming Loan

Abstract This article modifies the Support Vector Machine (SVM) algorithm to address the issue of a large number of explantory variables in the analysis of nonperforming loan recovery. First, the stepwise SVM is employed in the selection of model structure. Secondly, the results of linear stepwise regression are used as the initial states of the model selection. Empirical results show that the method not only achieves high accurate out-sample prediction, but also stable performance with in-samples and out-samples.