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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(2): 123-129 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.2.585

Using Recall and Elimination Terms in Separate Runs for High Volume Document Sorting

Harvey Hyman, Terry Sincich, Rick Will, and Warren Fridy III
Abstract—This paper reports on a study undertaken to explore the problem of volume in searching large scale digital collections. An experiment is conducted using elimination terms as a method to reduce the number of non-relevant documents in the information retrieval (IR) result. The goal is to provide insight into how elimination terms can be used as a sorting method to reduce volume. The results of the experiment demonstrate that modifying the search structure with an elimination term component can significantly reduce the number of non-relevant documents in the retrieval set to address the problem of high volume in electronic document sorting and searching tasks.

Index Terms—Information retrieval, knowledge discovery, search methods, document sorting.

Harvey Hyman is with New College of Florida, 5800 Bay Shore Road, Sarasota, Florida 34243, USA (e-mail: hhyman@NCF.edu).
Terry Sincich and Rick Will are with University of South Florida, 4202 E. Fowler Avenue, Tampa, Florida 33620, USA (e-mail: tsincich@USF.edu, rwill@usf.edu).
Warren Fridy III is with H2 & WF3 Research, LLC 701 S. Howard Avenue, Tampa, Florida 33606, USA (e-mail: warren@h2wf3.com).

[PDF]

Cite: Harvey Hyman, Terry Sincich, Rick Will, and Warren Fridy III, "Using Recall and Elimination Terms in Separate Runs for High Volume Document Sorting," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 123-129, 2016.

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