TY  - JOUR
T1  - A Novel Approach Integrating Ranking Functions Discovery, Optimization and Inference to Improve Retrieval Performance
AU - Febba, Ronald AU - K. Agbele, Kehinde AU - O. Nyongesa, Henry AU - O. Adesina, Ademola 
JO  - International Journal of Soft Computing
VL  - 5
IS  - 3
SP  - 155
EP  - 163
PY  - 2010
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2010.155.163
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2010.155.163
KW  - Ranking function
KW  -information retrieval
KW  -evolutionary techniques
KW  -fuzzy inference system
KW  -data fusion method
AB  - The significant roles play by ranking function in the performance and success of Information Retrieval (IR) systems and search engines cannot be underestimated. Diverse ranking functions are available in IR literature. However, empirical studies show that ranking functions do not perform constantly well across different contexts (queries, collections, users). In this study, a novel three-stage integrated ranking framework is proposed for implementing discovering, optimizing and inference rankings used in IR systems. The first phase, discovery process is based on Genetic Programming (GP) approach which smartly combines structural and contents features in the documents while the second phase, optimization process is based on Genetic Algorithm (GA) which combines document retrieval scores of various well-known ranking functions. In the 3rd phase, Fuzzy inference proves as soft search constraints to be applied on documents. We demonstrate how these two features are combined to bring new tasks and processes within the three concept stages of integrated framework for effective IR.
ER  -