In this paper, we propose RankIP, the first immune programming (IP) based ranking function discovery approach.
SVMRank learns a linear ranking function f(x)=w T x.
Fuzzy problems are defuzzified by ranking function, graded mean integration value and (alpha)- cut methods.
In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
Fan et al. [13] compared seven UTB-REFs on ranking function discovery for Web search using genetic programming (GP).
The referred work uses graphs, existing web-based algorithms, and some propose a more specific ranking function.
In this paper, we propose a novel nonlinear ranking function representation scheme and compare this new design to the well-known Vector Space model.
The experiments showed that CBIR-GP and CBIR-AR have similar performance, and both outperformed CBIR-SVM generating a better image ranking function.
Then, the gloss degree or relative gloss degrees could be estimated by ranking function for any patient.
Ranking function is instrumental in affecting the performance of a search engine.
Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.
