A methodology for preprocessing data for application of case based reasoning

Abstract

Produce quality software inside expected time and low cost has been one of the main challenges in the software industry today. Therefore, it is fundamental make estimates of size, effort, resources, cost and time spent in the software development process. Predictive models such as models based on analogy can be an alternative, especially in small and medium sized software development, they need perform reliable estimates. In this paper, we propose a methodology for pre-processing of data for use in software effort estimation. The results of use this methodology, applying Case Based Reasoning -CBR, indicate that the preprocessing enables to improve the accuracy of estimates.

Medical Case Based Reasoning Frameworks

“…Richter and Weber (2013) concluded that cases should come from reliable sources. As a result, medical data pre-processing steps are the first and the foremost to improve the accuracy of CBR systems, which are based on EHR data (Borges et al, 2012). Data pre-processing step is achieved by integrating CBR with machine learning techniques (Gu et al, 2010), such as Artificial Neural Network (ANN), Fuzzy Logic, Genetic Algorithms (GA), and rough sets.…”