A Framework for Genetic-Based Fusion of Similarity Measures in Chemical Compound Retrieval

• Main Authors: Salim, Naomie, Mulyadi, Mercy Trinovianti
• Format: Conference or Workshop Item
• Language: English
• Published: 2005
• Online Access: http://eprints.utm.my/id/eprint/408/2/03.pdf; http://eprints.utm.my/id/eprint/408/; https://books.google.com.my/books/about/Proceedings_of_the_1st_International_Sym.html?id=UnlXoAEACAAJ&redir_esc=y
• Institution: Universiti Teknologi Malaysia

Abstract In chemical compound retrieval, much data fusion effort has been made to combine results from multiple similarities searching system. A fundamental problem in the data fusion approach is how to optimally combine the results obtained from various retrieval systems since there is no known guideline on the best fusion model that works for all type of data and activity .This paper proposes a framework of data fusion approach based on linear combinations of retrieval status values obtained from Vector Space Model and Probability Model system. A Genetic Algorithm(GA)-based approach is used to find the best linear combination of weights assigned to the scores of different retrieval system to get the most optimal retrieval performance.