An Innovative SVM for Wheat Seed Quality Estimation

摘要

As a popular leaning algorithm, Support Vector Machine (SVM) have been utilized to solve the problem of data mining and knowledge discovery. However, as far as some unbalanced data sets of multi-group are concerned, the classifier model trained by C-SVM always presents some imbalanced error-rates on separating samples. Based on analysis of Lagrange multiplier, the paper brings forward some novelty concepts including the outer boundary of group, Misleading-SV, prediction-error-rate, etc. An innovative SVM based on C-correction is formulated and a method for correcting slack constant C is designed. On the target of winter wheat seed geometric feature evaluation for quality gradation, the research team constructs some testing experiments for method validation. Analysis of accuracy contour suggests the proposal scheme is able to effectively separate seeds by their geometric property at an accuracy of 96.5%. In parallel with some known congeneric algorithms, contrast results evidences that as the data set with sparse samples is considered, the method for correcting slack constant can elevate the general separation precision of classifier prominently.