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Is there a way to organize some reference articles related to topics covered by RapidMiner on this Forum.
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Sven Van Poucke
Title:
SOFTWARE EFFORT PREDICTION: AN EMPIRICAL EVALUATION OF METHODS TO TREAT MISSING VALUES WITH RAPIDMINER ®
Creator:
Olga Fedotova ; Gladys Castillo ; Leonor Teixeira ; Helena Alvelos
Related to:
International Journal of Engineering Science and Technology, 2011, Vol.3(7), p.6064
Description:
Missing values is a common problem in the data analysis in all areas, being software engineering not an exception. Particularly, missing data is a widespread phenomenon observed during the elaboration of effort prediction models (EPMs) required for budget, time and functionalities planning. Current work presents the results of a study carried out on a Portuguese medium-sized software development organization in order to obtain a formal method for EPMs elicitation in development processes. This study focuses on the performanceevaluation of several regression-based EPMs induced from data after applying three different methods to treat missing values. Results show that regression imputation offers substantial improvements over traditional techniques (case deletion and mean substitution). All the machine learning methods were implemented in RapidMiner®, one of the leading open-source data mining applications.
Subjects:
Software Effort Prediction ; Missing Values Treatment ; Multiple Linear Regression.
Source:
Directory of Open Access Journals (DOAJ)
Identifier:
ISSN: 09755462
Is there a way to organize some reference articles related to topics covered by RapidMiner on this Forum.
Cheers
Sven Van Poucke
Title:
SOFTWARE EFFORT PREDICTION: AN EMPIRICAL EVALUATION OF METHODS TO TREAT MISSING VALUES WITH RAPIDMINER ®
Creator:
Olga Fedotova ; Gladys Castillo ; Leonor Teixeira ; Helena Alvelos
Related to:
International Journal of Engineering Science and Technology, 2011, Vol.3(7), p.6064
Description:
Missing values is a common problem in the data analysis in all areas, being software engineering not an exception. Particularly, missing data is a widespread phenomenon observed during the elaboration of effort prediction models (EPMs) required for budget, time and functionalities planning. Current work presents the results of a study carried out on a Portuguese medium-sized software development organization in order to obtain a formal method for EPMs elicitation in development processes. This study focuses on the performanceevaluation of several regression-based EPMs induced from data after applying three different methods to treat missing values. Results show that regression imputation offers substantial improvements over traditional techniques (case deletion and mean substitution). All the machine learning methods were implemented in RapidMiner®, one of the leading open-source data mining applications.
Subjects:
Software Effort Prediction ; Missing Values Treatment ; Multiple Linear Regression.
Source:
Directory of Open Access Journals (DOAJ)
Identifier:
ISSN: 09755462
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