Statistical analysis with missing data
Statistical analysis with missing data
Structured induction in expert systems
Structured induction in expert systems
Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis
IEEE Transactions on Software Engineering - Special Issue on Artificial Intelligence in Software Applications
Evaluating techniques for generating metric-based classification trees
Journal of Systems and Software - An Oregon workshop on software metrics
Designing Storage Efficient Decision Trees
IEEE Transactions on Computers
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Machine Learning
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Validating the ISO/IEC 15504 measures of software development process capability
Journal of Systems and Software
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Integrating Time Domain and Input Domain Analyses of Software Reliability Using Tree-Based Models
IEEE Transactions on Software Engineering
An Enhanced Neural Network Technique for Software Risk Analysis
IEEE Transactions on Software Engineering
Machine Learning
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Issues on the Effective Use of CBR Technology for Software Project Prediction
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Tree-Based Software Quality Estimation Models For Fault Prediction
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Induction over large data bases
Induction over large data bases
Dealing with Missing Software Project Data
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
An Evaluation of k-Nearest Neighbour Imputation Using Likert Data
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
A Short Note on Safest Default Missingness Mechanism Assumptions
Empirical Software Engineering
Resource-oriented software quality classification models
Journal of Systems and Software
Ensemble Imputation Methods for Missing Software Engineering Data
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Good methods for coping with missing data in decision trees
Pattern Recognition Letters
AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES
Applied Artificial Intelligence
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Credit risk analysis using a reliability-based neural network ensemble model
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Constructing an accurate effort prediction model is a challenge in software engineering. The development and validation of models that are used for prediction tasks require good quality data. Unfortunately, software engineering datasets tend to suffer from the incompleteness which could result to inaccurate decision making and project management and implementation. Recently, the use of machine learning algorithms has proven to be of great practical value in solving a variety of software engineering problems including software prediction, including the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper proposes a method for improving software effort prediction accuracy produced by a decision tree learning algorithm and by generating the ensemble using two imputation methods as elements. Benchmarking results on ten industrial datasets show that the proposed ensemble strategy has the potential to improve prediction accuracy compared to an individual imputation method, especially if multiple imputation is a component of the ensemble.