Comparing Software Cost Prediction Models by a Visualization Tool

2008 ◽  
Author(s):  
Nikolaos Mittas ◽  
Lefteris Angelis
Keyword(s):  
Prediction Models ◽  
Visualization Tool ◽  
Cost Prediction ◽  
Software Cost

scholarly journals Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

2021 ◽  
Vol 348 ◽  
pp. 01002
Author(s):  
Assia Najm ◽  
Abdelali Zakrani ◽  
Abdelaziz Marzak
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Prediction Models ◽  
Project Managers ◽  
Parameters Optimization ◽  
Support Vector ◽  
Features Selection ◽  
Selection Methods ◽  
Cost Prediction ◽  
Software Cost

The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.

scholarly journals House Price Prediction Using Machine Learning

2019 ◽  
Vol 8 (9) ◽  
pp. 717-722 ◽  
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Housing Cost ◽  
Prediction Methods ◽  
Machine Learning Algorithm ◽  
Lasso Regression ◽  
Cost Prediction ◽  
Price Prediction

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction that tend to the advancement of real estate policies schemes. This study utilizes machine learning algorithms as a research method that develops housing price prediction models. We create a housing cost prediction model In view of machine learning algorithm models for example, XGBoost, lasso regression and neural system on look at their order precision execution. We in that point recommend a housing cost prediction model to support a house vender or a real estate agent for better information based on the valuation of house. Those examinations exhibit that lasso regression algorithm, in view of accuracy, reliably outperforms alternate models in the execution of housing cost prediction.

A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost Estimation

2018 ◽  
pp. 345-372
Author(s):  
Lefteris Angelis ◽  
Nikolaos Mittas ◽  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Prediction Models ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Regression Imputation ◽  
Mean Imputation ◽  
Regression Error ◽  
Visualization Techniques ◽  
Missing Data Techniques

Software Cost Estimation (SCE) is a critical phase in software development projects. However, due to the growing complexity of the software itself, a common problem in building software cost models is that the available datasets contain lots of missing categorical data. The purpose of this chapter is to show how a framework of statistical, computational, and visualization techniques can be used to evaluate and compare the effect of missing data techniques on the accuracy of cost estimation models. Hence, the authors use five missing data techniques: Multinomial Logistic Regression, Listwise Deletion, Mean Imputation, Expectation Maximization, and Regression Imputation. The evaluation and the comparisons are conducted using Regression Error Characteristic curves, which provide visual comparison of different prediction models, and Regression Error Operating Curves, which examine predictive power of models with respect to under- or over-estimation.

A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost Estimation

2015 ◽  
pp. 71-97
Author(s):  
Lefteris Angelis ◽  
Nikolaos Mittas ◽  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Prediction Models ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Regression Imputation ◽  
Mean Imputation ◽  
Regression Error ◽  
Visualization Techniques ◽  
Missing Data Techniques

Software Cost Estimation (SCE) is a critical phase in software development projects. However, due to the growing complexity of the software itself, a common problem in building software cost models is that the available datasets contain lots of missing categorical data. The purpose of this chapter is to show how a framework of statistical, computational, and visualization techniques can be used to evaluate and compare the effect of missing data techniques on the accuracy of cost estimation models. Hence, the authors use five missing data techniques: Multinomial Logistic Regression, Listwise Deletion, Mean Imputation, Expectation Maximization, and Regression Imputation. The evaluation and the comparisons are conducted using Regression Error Characteristic curves, which provide visual comparison of different prediction models, and Regression Error Operating Curves, which examine predictive power of models with respect to under- or over-estimation.

scholarly journals Research and Development Talents Training in China Universities—Based on the Consideration of Education Management Cost Planning

2021 ◽  
Vol 13 (17) ◽  
pp. 9583
Author(s):  
Long-Hao Yang ◽  
Biyu Liu ◽  
Jun Liu
Keyword(s):  
Expert System ◽  
Research And Development ◽  
Prediction Models ◽  
Management Policy ◽  
Cost Prediction ◽  
Education Management ◽  
Depth Analysis ◽  
Cost Planning ◽  
First Time ◽  
Significant Regional Difference

Research and development (R&D) talents training are asymmetric in China universities and can be of great significance for economic and social sustainable development. For the purpose of making an in-depth analysis in the education management costs for R&D talents training, the belief rule-based (BRB) expert system with data increment and parameter learning is developed to achieve education management cost prediction for the first time. In empirical analysis, based on the BRB expert system, the past investments and future planning of education management costs are analyzed using real education management data from 2001 to 2019 in 31 Chinese provinces. Results show that: (1) the existing education management cost investments have a significant regional difference; (2) the BRB expert system has excellent accuracy over some existing cost-prediction models; and (3) without changing the current education management policy and education cost input scheme, the regional differences in China’s education management cost input always exist. In addition to the results, the present study is helpful for providing model supports and policy references for decision makers in making well-grounded plans of R&D talents training at universities

Cost prediction models for the comparison of two groups

2001 ◽  
Vol 10 (4) ◽  
pp. 363-366 ◽  
Author(s):  
Andrew R. Willan ◽  
Bernie J. O'Brien
Keyword(s):  
Prediction Models ◽  
Cost Prediction
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