Algorithm of RB-Pred

The support vector machines (SVM) are universal approximator based on statistical and optimising theory. The SVM is particularly attractive to biological analysis due to its ability to handle noise, large dataset and large input spaces. The SVM has been shown to perform better in protein secondary structure, MHC and TAP binder prediction and analysis of microarray data. The basic idea of SVM can be described as follows: first, the inputs are formulated as feature vectors. Secondly, these feature vectors are mapped into a feature space by using the kernel function. Thirdly, a division is computed in the feature space to optimally separate to classes of training vectors. The SVM always seeks global hyperplane to separate the both classes of examples in training set and avoid overfitting. This property of SVM is more superior in comparison to other machine learning techniques which are based on artificial intelligence.

In the present study, we have used regression module of SVM called support vector regression (SVR) implemented through the software SVM-light to predict the leaf blast severity (%) in rice. The software enables the users to define six weather parameters which are trained by our SVM model on the basis of its kernel functions, including linear, RBF and polynomial. The ith SVM was trained with all the samples in the ith class with positive labels and all other samples with negative labels. An unknown sample was classified into the class that corresponded to the SVM with the highest output score as the machine learning techniques are more successful if input units/patterns are of fixed length.

ALGORITHM of RB-Pred