Global mutual information-based feature selection approach using single-objective and multi-objective optimization

Han, Min; Ren, Weijie

doi:10.1016/j.neucom.2015.06.016

Cited by 61 publications

(16 citation statements)

References 40 publications

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“…The calculation of probability distribution function is significant for evaluating MI between two rule neurons. At present, there are several frequently used methods to evaluate MI, such as copula entropy method, kernel method and k-nearest neighbor method [16]. Due to the computational…”

Section: Mutual Informationmentioning

confidence: 99%

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

Qiao

2020

SN Appl. Sci.

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The fine particulate matter (PM 2.5) problem has become one of the biggest topics of concern in many cities, such as Beijing in China. This study attempts to employ a self-organizing fuzzy neural network (SOFNN) to predict the hourly concentration of PM 2.5 using multi-source data. The multi-source data including meteorological data, pollutant concentration data and image data are first obtained through different methods. Then, a novel SOFNN, which is optimized by an improved gradient descent algorithm, is proposed based on the mutual information (MI) and sensitivity analysis (SA). More importantly, MI and SA are introduced to effectively adjust the structure of fuzzy neural network without changing the performance of the original network. Finally, the MI-and SA-based SOFNN is deployed to infer PM 2.5 concentration at the study area. The experimental results show that our proposed model can achieve a more compact network structure, higher prediction performance and lower computation time as compared with state-of-the-art or popular methods.

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Section: Mutual Informationmentioning

confidence: 99%

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

Qiao

2020

SN Appl. Sci.

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“…Since the dimension of candidate features maybe high, the time consumption cannot be neglected [26]. We adopt a greedy stepwise search algorithm to find a best C starting with a small set of definite features and adding one another feature at one time [44], and only if the data of r sz increases, the new feature will be accepted, and until there is no improvement or no features to add, it stops to identify the most relevant subset which contains the major relevant factors of PV module temperature.…”

Section: Correlation-based Feature Selectionmentioning

confidence: 99%

Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

Sun

Wang

et al. 2016

Energies

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Abstract:The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV) systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI) and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (MIFs) and PV module temperature from both quality and quantitative aspects. The identification and confirmation of primary MIFs of PV module temperature are investigated as the first step of this research from the perspective of physical meaning and mathematical analysis about electrical performance and thermal characteristic of PV modules based on PV effect and heat transfer theory. Furthermore, the quantitative description of the MIFs influence on PV module temperature is mathematically formulated as several indexes using correlation-based feature selection (CFS) and MI theory to explore the specific impact degrees under four different typical weather statuses named general weather classes (GWCs). Case studies for the proposed methods were conducted using actual measurement data of a 500 kW grid-connected solar PV plant in China. The results not only verified the knowledge about the main MIFs of PV module temperatures, more importantly, but also provide the specific ratio of quantitative impact degrees of these three MIFs respectively through CFS and MI based measures under four different GWCs.

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“…The common methods to calculate MI include copula entropy method [32], Kernel method [33], k-nearest neighbor method [34] and histogram method [35,36]. In this paper, we adopt histogram method to estimate MI because it is the fastest and simplest method among them.…”

Section: Mutual Information Estimation Of Sfnnmentioning

confidence: 99%

Mutual information based weight initialization method for sigmoidal feedforward neural networks

Qiao

2016

Neurocomputing

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Global mutual information-based feature selection approach using single-objective and multi-objective optimization

Cited by 61 publications

References 40 publications

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

Mutual information based weight initialization method for sigmoidal feedforward neural networks

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