DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions

Computer Science – Software Engineering

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

3d Artificial Intelligence Tecniques in Software Engineering Workshop,7 October,2010, Larnaca, Cyprus

Scientific paper

Case Based Reasoning and particularly Estimation by Analogy, has been used in a number of problem-solving areas, such as cost estimation. Conventional methods, despite the lack of a sound criterion for choosing nearest projects, were based on estimation using a fixed and predetermined number of neighbors from the entire set of historical instances. This approach puts boundaries to the estimation ability of such algorithms, for they do not take into consideration that every project under estimation is unique and requires different handling. The notion of distributions of distances together with a distance metric for distributions help us to adapt the proposed method (we call it DD-EbA) each time to a specific case that is to be estimated without loosing in prediction power or computational cost. The results of this paper show that the proposed technique achieves the above idea in a very efficient way.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-428195

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.