Feature selection in multimedia: The state-of-the-art review

Lee, Pui Yi; Loh, Wei Ping; Feng, Chin Jeng

doi:10.1016/j.imavis.2017.09.004

Cited by 34 publications

(10 citation statements)

References 112 publications

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“…FS refers to a feature-generation process that automatically searches for an optimal FF subset from the complete set by comparing the FDD performance of all possible subsets [57]. FS does not produce new FFs; it retains only the essential modeling features but discard unnecessary ones from the original dataset.…”

Section: ) Feature Selection (Fs)mentioning

confidence: 99%

“…To determine the minimum feature subset that has the most positive effects on improving data-based FDD accuracy while incurring the fewest negative impacts on model efficiency [56]. FE has been widely studied for applications in fields such as multimedia [57], bioinformatics [58][59][60], industrial process monitoring [61][62][63] and renewable-energy data mining [56,[64][65][66][67][68]. However, obtaining accurate and costefficient FE results is challenging for HVAC&R FDD.…”

Section: Introductionmentioning

confidence: 99%

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Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Liu

et al. 2021

IEEE Access

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Section: ) Feature Selection (Fs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Liu

et al. 2021

IEEE Access

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“…A comprehensive and extensive review of over various relevant works was carried out by researchers. The scope, applications and restrictions of these works were also investigated [26][27][28]. Some other related works are as below: Unsupervised feature selection methods [29][30][31], feature selection using a variable number of features [32], connecting data characteristics using feature selection [33][34][35][36], a new method for feature selection using feature self-representation and a low-rank representation [36], integrating feature selection algorithms [37], financial distress prediction using feature selection [38], and feature selection based on a Morisita estimator for regression problems [39].…”

Section: (Iv) Online-basedmentioning

confidence: 99%

FeatureSelect: a software for feature selection based on machine learning approaches

2019

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Background: Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. Results: In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods. Conclusions: FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements.

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“…LASSO and LARS regularisation) [92]. There are also several specific methods such as sparse dictionary learning [93] applied in video processing that are rarely used for traffic forecasting. Adopting these methods for spatiotemporal traffic forecasting is possibly a promising research direction.…”

Section: Spatiotemporal Fse Applied In Related Areasmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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Feature selection in multimedia: The state-of-the-art review

Cited by 34 publications

References 112 publications

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

FeatureSelect: a software for feature selection based on machine learning approaches

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

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