Wrapper Feature Selection Algorithm for the Optimization of an Indicator System of Patent Value Assessment

TL;DR

This paper introduces a wrapper feature selection algorithm based on classifier accuracy to optimize patent value assessment systems, reducing feature set size and improving prediction performance.

Contribution

It develops a novel wrapper-mode feature selection algorithm tailored for patent value assessment, enhancing system efficiency and accuracy.

Findings

Algorithm reduces feature set size effectively.

Significantly improves classifier prediction accuracy.

Optimizes patent value indicator system.

Abstract

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.