Segmentation of Software Engineering Datasets Using the M5 Algorithm

Rodríguez, Dario; Cuadrado, Juan; Sicilia, Miguel Ángel; Ruíz, Roberto

doi:10.1007/11758549_106

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…From the two types of decision trees, namely regression trees and classifier trees, the former was selected. Decision trees are known as regression trees when output values comply with intervals (Rodríguez et al, 2006). In data mining, a decision tree is used as a tool to describe the data rather than to make decisions.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of the compression ratio for municipal solid waste using decision tree

Mokhtari

Rad

2013

Waste Manag Res

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The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

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Section: Methodsmentioning

confidence: 99%

“…Decision trees are known as regression trees when output values comply with intervals (Rodríguez et al, 2006). From the two types of decision trees, namely regression trees and classifier trees, the former was selected.…”

Section: Decision Treementioning

confidence: 99%

Prediction of the compression ratio for municipal solid waste using decision tree

Mokhtari

Rad

2013

Waste Manag Res

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“…In the literature of software development effort estimation, segmented models are the only models that have localised the estimation process [28, 29]. The authors have proposed a model in which the projects are divided into several clusters using a clustering algorithm (Expectation‐Maximization (EM) and M5).…”

Section: Related Workmentioning

confidence: 99%

Model to estimate the software development effort based on in‐depth analysis of project attributes

Khatibi

Bardsiri

2015

IET softw.

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Over the past years, numerous models have been proposed to estimate the development effort in the early stages of a software project. The existing models have mostly relied on soft computing techniques and weighting methods. Although they have reduced the complexity and vagueness of software project attributes, attempts are ongoing to develop more accurate and reliable estimation models. This paper is concentrated on selective classification of software projects based on underlying attributes to localise the development effort estimation process in a widely used model called analogy-based estimation (ABE). The proposed model is a combination of ABE, selective classification and a weighting system in which the attributes of different software projects are assigned different weights. In fact, the process of attribute weighting is customised based on the nature of project being estimated. A real data set was utilised to evaluate the performance of the proposed model. A comparison between the estimates achieved by the proposed model and those obtained from other well-known effort estimation models showed that the proposed model substantially improves the performance metrics. Along with the improvement of accuracy, the proposed model is able to be used in an extensive domain of software projects.

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“…In this study, we apply the genetic programming framework in two software engineering domains, aiming to estimate the required effort of software projects. The application of data mining models has become prevailing recently in software engineering, carrying competitive results that can provide advantages to project managers (Rodriguez et al, 2006) (Menzies and Di Stefano, 2004). In (Shepperd and Schofield, 1997), a case-based reasoning approach is examined and compared with a set of regression models.…”

Section: Introductionmentioning

confidence: 99%

Deriving Models for Software Project Effort Estimation by Means of Genetic Programming

Tsakonas

Dounias

2009

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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This paper presents the application of a computational intelligence methodology in effort estimation for software projects. Namely, we apply a genetic programming model for symbolic regression; aiming to produce mathematical expressions that (1) are highly accurate and (2) can be used for estimating the development effort by revealing relationships between the project's features and the required work. We selected to investigate the effectiveness of this methodology into two software engineering domains. The system was proved able to generate models in the form of handy mathematical expressions that are more accurate than those found in literature.

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Segmentation of Software Engineering Datasets Using the M5 Algorithm

Cited by 8 publications

References 7 publications

Prediction of the compression ratio for municipal solid waste using decision tree

Prediction of the compression ratio for municipal solid waste using decision tree

Model to estimate the software development effort based on in‐depth analysis of project attributes

Deriving Models for Software Project Effort Estimation by Means of Genetic Programming

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