A hybrid cost estimation framework based on feature-oriented data mining approach

Sajadfar, Narges; Ma, Yongsheng

doi:10.1016/j.aei.2015.06.001

Cited by 34 publications

(15 citation statements)

References 26 publications

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“…Filter approach is computationally simple and fast. Due to its independence of the mining algorithm, the features selection needs to be performed only once, and then different classifiers can be assessed [7]. Examples of the filter based approaches are given in Table 1.…”

Section: Features Selectionmentioning

confidence: 99%

“…Many data mining algorithms don't perform well with those irrelevant attributes or features. Therefore, features selection techniques are used to remove those non-informative features before the use of the mining algorithm [7]. The main objectives of features selection techniques are to avoid overfitting and improve the performance of the model, which leads to faster and more cost-effective models [7].…”

Section: Features Selectionmentioning

confidence: 99%

“…Several prediction algorithms don't perform well with large amounts of features, while many ineffective features may be presented in electrical load data [7]. Feature selection has a good impact in improving the model performance as well as providing faster decisions as it minimizes the considered features to the only effective ones [7]. On the other hand, training of the forecasting model with raw data that usually contains outliers certainly degrades the system's performance as the decisions are affected by those rare data whose behavior is very exceptional.…”

Section: Introductionmentioning

confidence: 99%

“…[6]. Several prediction algorithms don't perform well with large amounts of features, while many ineffective features may be presented in electrical load data [7]. Feature selection has a good impact in improving the model performance as well as providing faster decisions as it minimizes the considered features to the only effective ones [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A data mining based load forecasting strategy for smart electrical grids

Saleh

Rabie

Abo-Al-Ez

2016

Advanced Engineering Informatics

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Section: Features Selectionmentioning

confidence: 99%

Section: Features Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A data mining based load forecasting strategy for smart electrical grids

Saleh

Rabie

Abo-Al-Ez

2016

Advanced Engineering Informatics

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“…The final proposed model could be repeatable, robust, and accurate. Besides, data mining needs to be supported with available and comprehensive business or manufacture process data for implementation (Sajadfar & Ma, 2015).…”

Section: Changes Of Managerial Accounting Functions Cost Managementmentioning

confidence: 99%

Integrating Data Mining Into Managerial Accounting System: Challenges and Opportunities

Wang¹,

Wang²

2016

CBR

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Data mining involves extracting information from large data sets, discovering the hidden relationships and unknown dependencies, and supporting strategic decision-making tasks. The alignment of data mining and business would bring benefits to the organization's management. The study investigated the adoption of data mining technologies in managerial accounting system, concentrating on the challenges and opportunities. The research showed that with the technology adoption, managerial functions could be improved and current information system could be upgraded. Since the technical progresses are reshaping the world of business and accountancy, it is significant for accountants and finance professionals to exploit information technologies.

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