On the value of parameter tuning in heterogeneous ensembles effort estimation

Hosni, Mohamed; Idri, Ali; Abran, Alain; Nassif, Ali Bou

doi:10.1007/s00500-017-2945-4

Cited by 64 publications

(60 citation statements)

References 91 publications

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“…The SDEE literature defines 2 types of EEE: 1) Homogeneous ensembles that refer to 2 subtypes: (a) Ensembles combining 1 single technique with at least 2 different configurations (b) Ensembles combining 1 single technique with 1 ensemble learning such as bagging, 52 negative correlation, 53 or random subspace 21,54 ; and 2) Heterogeneous ensembles, which refer to an ensemble that combines at least 2 different single techniques. 1,55 Idri et al 24,56 performed a systematic map and literature review of 24 studies on the use of EEE published between 2000 and 2016. The main conclusions were as follows:…”

Section: Ensemble Methods In Effort Estimationmentioning

confidence: 99%

“…This section describes the measures used to assess the performance of single and ensemble FA estimation techniques from 2 perspectives: Standardized accuracy (SA) and effect size assess the reasonability of a given estimation technique, ie, they check whether the estimation technique is actually predicting or guessing with respect to a baseline model; The other 5 performance criteria evaluate the accuracy of the estimation technique. …”

Section: Empirical Designmentioning

confidence: 99%

“…Ensemble effort estimation (EEE) combines at least 2 single estimation techniques and aggregates their outputs by means of a combination rule. The SDEE literature defines 2 types of EEE: Homogeneous ensembles that refer to 2 subtypes: Ensembles combining 1 single technique with at least 2 different configurations Ensembles combining 1 single technique with 1 ensemble learning such as bagging, negative correlation, or random subspace; and Heterogeneous ensembles, which refer to an ensemble that combines at least 2 different single techniques …”

Section: Introductionmentioning

confidence: 99%

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Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Hosni

Idri

Abran

2018

J Software Evolu Process

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Researchers have developed and evaluated many techniques to deliver accurate estimates of the effort required to complete a new software program. Among these, analogy has emerged as a very promising technique, in particular the fuzzy analogy estimation technique that uses the fuzzy logic concepts in order to deal with both categorical and numerical data. The aim of this paper is twofold: (1) evaluate the impact of 3 filters on the predictive ability of single and ensemble fuzzy analogy techniques and (2) assess whether filters could be a source of diversity for fuzzy analogy ensembles. Moreover, it compares the filter single and ensemble fuzzy analogy techniques with fuzzy analogy ensembles built without using feature selection over 6 datasets. The overall results suggest that (1) more accurate estimates are generated when filters were used with single and ensemble fuzzy analogy techniques, (2) filter single fuzzy analogy techniques outperformed filter fuzzy analogy ensembles, and (3) fuzzy analogy ensembles without feature selection were more accurate than filter single and ensemble techniques. Therefore, though the use of feature selection techniques led single and ensemble fuzzy analogy to generate accurate estimations, they failed to be a source of diversity for fuzzy analogy ensembles. Hence, constructing fuzzy analogy homogenous ensembles that combine single fuzzy analogy techniques with different parameter configurations still generate better accuracy than filter fuzzy analogy ensembles. However, further empirical evaluations of filter/wrappers fuzzy analogy ensembles are required in order to confirm or refute these findings.

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Section: Ensemble Methods In Effort Estimationmentioning

confidence: 99%

Section: Empirical Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Hosni

Idri

Abran

2018

J Software Evolu Process

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“…Although the COCOMO81 dataset is now over 30 years old, it is still commonly used to evaluate the performance of new techniques, especially in comparative studies . COCOMO81 dataset includes 63 software projects described by 17 cost drivers (eg, features), 15 of which are on a scale consisting of six linguistic values: “very low,” “low”, “nominal”, “high”, “very high,” and “extra high.” The two remaining cost drivers are the software size, measured in KDSI (kilo delivered source instructions), and the project mode, which comprises three categories: “organic,” “semi‐detached,” and “embedded.” In this study, all projects and features of the COCOMO81 dataset were used.…”

Section: Empirical Designmentioning

confidence: 99%

Analysis of cluster center initialization of 2FA‐kprototypes analogy‐based software effort estimation

Amazal

Idri²,

Abran

2019

J Software Evolu Process

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Analogy‐based estimation is one of the most widely used techniques for effort prediction in software engineering. However, existing analogy‐based techniques suffer from an inability to correctly handle nonquantitative data. To deal with this limitation, a new technique called 2FA‐kprototypes was proposed and evaluated. 2FA‐kprototypes is based on the use of the fuzzy k‐prototypes clustering technique. Although fuzzy k‐prototypes algorithms are well known for their efficiency in clustering numerical and categorical data, they are sensitive to the selection of initial cluster centers. In this paper, the impact of cluster center initialization on improving the prediction accuracy of 2FA‐kprototypes was analyzed and discussed using two cluster initialization techniques: centrality‐based initialization and density‐based initialization. The performance of 2FA‐kprototypes using these two initialization techniques was evaluated and compared with that of 2FA‐kprototypes using random initialization over four datasets: ISBSG, COCOMO81, USP05‐FT, and USP05‐RQ. The results showed an improvement in the performance of 2FA‐kprototypes in terms of estimation accuracy when the all‐in method is used.

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“…-Even in the field of big data, a large portion of the data is not spatial or temporal correlated, leading to the fact that most DNNs cannot be applied directly. To fully exploit the large training sets, researchers have to utilize ordinary learning models [23,30,48], such as linear or generalized linear models, support vector machine, random forest, etc. Compared with DNNs, these models are structurally simple and relatively less inclined to overfit [15,45,47].…”

Section: Introductionmentioning

confidence: 99%

Dense adaptive cascade forest: a self-adaptive deep ensemble for classification problems

et al. 2019

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Recent researches have shown that deep forest ensemble achieves a considerable increase in classification accuracy compared with the general ensemble learning methods, especially when the training set is small. In this paper, we take advantage of deep forest ensemble and introduce the Dense Adaptive Cascade Forest (daForest). Our model has a better performance than the original Cascade Forest with three major features: first, we apply SAMME.R boosting algorithm to improve the performance of the model. It guarantees the improvement as the number of layers increases. Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration. Third, we add a hyper-parameters optimization layer before the first classification layer, making our model spend less time to set up and find the optimal hyper-parameters. Experimental results show that daForest performs significantly well, and in some cases, even outperforms neural networks and achieves state-of-the-art results.

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On the value of parameter tuning in heterogeneous ensembles effort estimation

Cited by 64 publications

References 91 publications

Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Analysis of cluster center initialization of 2FA‐kprototypes analogy‐based software effort estimation

Dense adaptive cascade forest: a self-adaptive deep ensemble for classification problems

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