Wrapper Feature Selection Algorithm for the Optimization of an Indicator System of Patent Value Assessment
This work addresses the need for efficient patent value assessment to support patent transactions and technology application, but it is incremental as it builds on existing feature selection methods.
The authors tackled the problem of patent value assessment by developing a wrapper-mode feature selection algorithm based on classifier prediction accuracy, which reduced feature set size and significantly enhanced prediction accuracy in experiments on UCI datasets, and when applied to patent value assessment, it reduced the indicator system size and improved classifier generalization.
Effective patent value assessment provides decision support for patent transection and promotes the practical application of patent technology. The limitations of previous research on patent value assessment were analyzed in this work, and a wrapper-mode feature selection algorithm that is based on classifier prediction accuracy was developed. Verification experiments on multiple UCI standard datasets indicated that the algorithm effectively reduced the size of the feature set and significantly enhanced the prediction accuracy of the classifier. When the algorithm was utilized to establish an indicator system of patent value assessment, the size of the system was reduced, and the generalization performance of the classifier was enhanced. Sequential forward selection was applied to further reduce the size of the indicator set and generate an optimal indicator system of patent value assessment.