MINN: A Missing Data Imputation Technique for Analogy-based Effort Estimation

Shah, Muhammad Arif; Dayang, N; Adham, Mohd; Wakil, Karzan; Younas, Muhammad; Mustafa, A. F.

doi:10.14569/ijacsa.2019.0100230

Cited by 2 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Desharnais dataset as one of the most common datasets in the field of software effort estimation [51]. Recent research studies investigate Desharnais dataset imputation for ABE performance evaluation [39,42,44]. The data contain 81 software projects related to Canadian Software Company, 77 projects are complete with no missing values, and four projects are considered incomplete with some missing values.…”

Section: A Data Sets Descriptionmentioning

confidence: 99%

“…5, it is consists of four main steps: generating missing values, missing data imputation, ABE effort estimation, and accuracy evaluation. The design for the used empirical process followed similar approach used in [18,39,44] for evaluating the impact of MD imputation for ABE performance prediction.…”

Section: Empirical Processmentioning

confidence: 99%

“…In this experimental study, we investigate Desharnais dataset as one of the most common datasets in the field of software effort estimation. Recent research studies investigate Desharnais dataset imputation for ABE performance evaluation [39,42,44]. Desharnais dataset is considered relatively small with 81 software projects only, and contained only numerical attributes, these might be considered as external threats, Table XIII.…”

Section: B External Validitymentioning

confidence: 99%

“…Abnane and Idri[43] investigated mixed (Numerical and categorical) MD imputation techniques for ABE model, imputation techniques achieved better accuracy results, there is no significant difference between SVR and KNNI for mixed MD imputation. Muhammad Arif Shah[44] proposed Median Imputation of the Nearest Neighbor (MINN) for ABE mode , the investigation of the proposed model under Desharnais dataset outperformed both MI and KNN under MNAR mechanism.Abnane, Hosni et al[45] optimize parameters of KNN imputation using grid search, the optimize KNN imputation improved ABE significantly compared with regular KNN imputation. Abnane, Idri et al[46] Proposed 2FA-KP-I (Fuzzy Analogy k-Prototypes Imputation) to impute mixed MD in ABE model, 2FA-KP-I outperformed KNNI under different missing ratio and MD mechanisms for ABE in the studied datasets.Table I introduced literature review of MD techniques used in ABE model, it also summarized the type of MD, imputation methods used MD mechanism, and the findings for each study.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy C-mean Missing Data Imputation for Analogy-based Effort Estimation

AlMutlaq¹,

Jawawi²,

Arbain³

2021

IJACSA

View full text Add to dashboard Cite

The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model.

show abstract

Section: A Data Sets Descriptionmentioning

confidence: 99%

Section: Empirical Processmentioning

confidence: 99%

Section: B External Validitymentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy C-mean Missing Data Imputation for Analogy-based Effort Estimation

AlMutlaq¹,

Jawawi²,

Arbain³

2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…The soft computing based estimation by analogy was initiated by Idri and Abran [13], and it is still the interest of many researchers. Most of the studies in this research domain adopted MMRE and PRED (.25) as the performance evaluation metric for comparing and ranking the estimation models even though these performance measures are much criticized by the domain researchers [Foss,Stensrud [14] [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Adopting the Appropriate Performance Measures for Soft Computing-based Estimation by Analogy

Shah

Jawawi

Isa

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

Self Cite

View full text Add to dashboard Cite

Soft Computing based estimation by analogy is a lucrative research domain for the software engineering research community. There are a considerable number of models proposed in this research area. Therefore, researchers are of interest to compare the models to identify the best one for software development effort estimation. This research showed that most of the studies used mean magnitude of relative error (MMRE) and percentage of prediction (PRED) for the comparison of their estimation models. Still, it was also found in this study that there are quite a number of criticisms done on accuracy statistics like MMRE and PRED by renowned authors. It was found that MMRE is an unbalanced, biased, and inappropriate performance measure for identifying the best among competing estimation models. The accuracy statistics, e.g., MMRE and PRED, are still adopted in the evaluation criteria by the domain researchers, stating the reason for "widely used," which is not a valid reason. This research study identified that, since there is no practical solution provided so far, which could replace MMRE and PRED, the researchers are adopting these measures. The approach of partitioning the large dataset into subsamples was tried in this paper using estimation by analogy (EBA) model. One small and one large dataset were considered for it, such as Desharnais and ISBSG release 11. The ISBSG dataset is a large dataset concerning Desharnais. The ISBSG dataset was partitioned into subsamples. The results suggested that when the large datasets are partitioned, the MMRE produces the same or nearly the same results, which it produces for the small dataset. It is observed that the MMRE can be trusted as a performance metric if the large datasets are partitioned into subsamples.

show abstract

MINN: A Missing Data Imputation Technique for Analogy-based Effort Estimation

Cited by 2 publications

References 26 publications

Fuzzy C-mean Missing Data Imputation for Analogy-based Effort Estimation

Fuzzy C-mean Missing Data Imputation for Analogy-based Effort Estimation

Adopting the Appropriate Performance Measures for Soft Computing-based Estimation by Analogy

Contact Info

Product

Resources

About