GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

Oliveira, Adriano L. I.; Braga, Petrônio L.; Lima, Ricardo; Cornélio, Márcio

doi:10.1016/j.infsof.2010.05.009

Cited by 194 publications

(128 citation statements)

References 26 publications

(125 reference statements)

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“…Unstructured data ( Image , Audio ,Video, text ) [28], [29], [30], [31] Software Effort Estimation Software fault prediction Software Engineering [33]. [34] Classification of power system disturbances Power Sector [35] Intrusion Detection Network Security [36], [37], [38] Human identification by gait Computer Vision [39] While we look at the application domains, two domains that stand out in terms of number and variety of application are unstructured data and bioinformatics and medical application, the common linkage that can't be missed is high dimensionality of both these domains.…”

Section: Application / Business Domain Referencementioning

confidence: 99%

Feature Selection: A Practitioner View

Goswami¹,

Chakrabarti²

2014

IJITCS

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Abstract-Feature selection is one of the most important preprocessing steps in data mining and knowledge Engineering. In this short review paper, apart from a brief taxonomy of current feature selection methods, we review feature selection methods that are being used in practice. Subsequently we produce a near comprehensive list of problems that have been solved using feature selection across technical and commercial domain. This can serve as a valuable tool to practitioners across industry and academia. We also present empirical results of filter based methods on various datasets. The empirical study covers task of classification, regression, text classification and clustering respectively. We also compare filter based ranking methods using rank correlation.

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Section: Application / Business Domain Referencementioning

confidence: 99%

Feature Selection: A Practitioner View

Goswami¹,

Chakrabarti²

2014

IJITCS

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“…Many popular methods, such as SVM [8], ANN [3][4][5], and GM [6,7], could be used to construct the member prediction function. The combined prediction function H is deducted from the member functions H 1 , H 2 , ⋅⋅⋅, H c by a weighted mean method as below [13]:…”

Section: Basic Process Of Baggingmentioning

confidence: 99%

Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data

Yao

Sun

et al. 2011

Energies

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Abstract:The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method. The generalization ability of the E-Bagging is enhanced significantly by the comprehensive information entropy. A total of sets of 1200 oil-dissolved gas data of transformers are used as examples of fault prediction. The comparisons between the E-Bagging and the traditional Bagging and individual prediction approaches are presented. The results show that the E-Bagging possesses higher accuracy and greater stability of prediction than the traditional Bagging and individual prediction approaches.

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“…In recent years, machine learning (ML) techniques such as neural networks [3,26] bagging predictors [6] and support vector regression (SVR) [10,25] have been investigated to develop SEE models. Some researchers [14,15] have considered ML based method as a major category of SEE methods.…”

Section: Introductionmentioning

confidence: 99%