Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Miao, Ran; Gong, Dunwei; Gao, Xiao‐Zhi

doi:10.1109/access.2019.2894366

Cited by 90 publications

(48 citation statements)

References 186 publications

(189 reference statements)

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“…PSO algorithm is inspired by a flock of birds seeking food. It treats each solution of the optimization problem as a bird that flies at a certain velocity in the search space, and its velocity is adjusted dynamically [38][39][40]. The bird is abstracted as a particle without weight and volume, and the location of the i-th particle in all the n dimensions is represented as…”

Section: Canonical Psomentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

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This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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Section: Canonical Psomentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

Self Cite

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“…Relief algorithms, one of the most successful preprocessing algorithms, can select attributes effectively without any assumption of attribute independence. However, they are restricted to two-class problems [45][46][47][48][49]. The Relief-F algorithm, which is extended from the relief algorithm and suitable for handling multi-class problems, is applied in this paper to further select attributes.…”

Section: Of 13mentioning

confidence: 99%

Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms

Meng

Zhang

2020

Energies

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In practical power system operation, knowing the voltage stability limits of the system is important. This paper proposes using a decision tree (DT) to extract guidelines through offline study results for assessing system voltage stability status online. Firstly, a sample set of DTs is determined offline by active power injection and bus voltage magnitude (P-V) curve analysis. Secondly, participation factor (PF) analysis and the Relief-F algorithm are used successively for attribute selection, which takes both the physical significance and the classification capabilities into consideration. Finally, the C4.5 algorithm is used to build the DT because it is more suitable for handling continuous variables. A practical power system is implemented to verify the feasibility of the proposed online voltage stability margin (VSM) assessment framework. Study results indicate that the operating guidelines extracted from the DT can help power system operators assess real time VSM effectively.

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“…Hence, it is recommended to adoption of dimensionality reduction or features selection techniques [38]. Dimensionality reduction techniques combine original features to provide a smaller number of features with enhanced predictive power.…”

Section: Feature Selectionmentioning

confidence: 99%

Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons

Stefani

Bontempi

et al. 2020

Technol Econ Smart Grids Sustain Energy

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The massive penetration of renewable power generation in modern power grids is an effective way to reduce the impact of energy production on global warming. Unfortunately, the wind power generation may affect the regular operation of electrical systems, due to the stochastic and intermittent nature of the wind. For this reason, reducing the uncertainty about the wind evolution, e.g. by using short-term wind power forecasting methodologies, is a priority for system operators and wind producers to implement low-carbon power grids. Unfortunately, though the complexity of this task implies the comparison of several alternative forecasting methodologies and dimensionality reduction techniques, a general and robust procedure of model assessment still lacks in literature. In this paper the authors propose a robust methodology, based on extensive statistical analysis and resampling routines, to supply the most effective wind power forecasting method by testing a vast ensemble of methodologies over multiple timescales and on a real case study. Experimental results on real data collected in an Italian wind farm show the potential of ensemble approaches integrating both statistical and machine learning methods.

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Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Cited by 90 publications

References 186 publications

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms

Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons

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