Non-linear variable selection for artificial neural networks using partial mutual information

May, Robert J.; Maier, Holger R.; Dandy, Graeme C.; Fernando, T.M.K.G.

doi:10.1016/j.envsoft.2008.03.007

Cited by 258 publications

(167 citation statements)

References 43 publications

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“…Moreover, it is able to capture all dependence between two variables and as it is a model-free strategy, it is not necessary to define a model structure a priori. PMI is a measure of the partial or additional dependence that a new input can add to the existing prediction model [25][26]. Given a dependent discrete variable Y (the output of the model), and an input X (the independent discrete variable), for a set of preexisting inputs Z, the discrete version of the PMI criterion is defined as: A good estimate of the expectation function is necessary for calculating the PMI criterion.…”

Section: Partial Mutual Informationmentioning

confidence: 99%

Investigating China’s Urban Air Quality Using Big Data, Information Theory, and Machine Learning

Chen¹,

Kan²,

Li³

et al. 2018

Pol. J. Environ. Stud.

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Section: Partial Mutual Informationmentioning

confidence: 99%

Investigating China’s Urban Air Quality Using Big Data, Information Theory, and Machine Learning

Chen¹,

Kan²,

Li³

et al. 2018

Pol. J. Environ. Stud.

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“…The use of sensitivity analysis, as was done by , is one way of achieving this. Alternatively, the use of techniques for the selection of inputs to data-driven models (Bowden et al, 2005a;Bowden et al, 2005b;Fernando et al, 2009;Galelli and Castelletti, 2013;Galelli et al, 2014;May et al, 2008a;May et al, 2008b) might be worthwhile. For example, these are at the core of selection-based dynamic emulation proposed by Castelletti et al (2012b).…”

Section: Since Inputs To Data-driven Sms Generally Include the Decisimentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

524

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…This MI score is then applied in the next step of the input variable selection method. The principle of Hampel distance is applied in this work using the following calculation principle (May, Maier, Dandy & Fernando, 2008). Hampel distance is defined as follows.…”

Section: (3)mentioning

confidence: 99%

A production throughput forecasting system in an automated hard disk drive test operation using GRNN

Samattapapong

Afzulpurkar

2016

JIEM

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Abstract:Purpose: The goal of this paper is to develop a pragmatic system of a production throughput forecasting system for an automated test operation in a hard drive manufacturing plant. The accurate forecasting result is necessary for the management team to response to any changes in the production processes and the resources allocations.Design/methodology/approach: In this study, we design a production throughput forecasting system in an automated test operation in hard drive manufacturing plant. The proposed system consists of three main stages. In the first stage, a mutual information method was adopted for selecting the relevant inputs into the forecasting model. In the second stage, a generalized regression neural network (GRNN) was implemented in the forecasting model development phase. Finally, forecasting accuracy was improved by searching the optimal smoothing parameter which selected from comparisons result among three optimization algorithms: particle swarm optimization (PSO), unrestricted search optimization (USO) and interval halving optimization (IHO).Findings: The experimental result shows that (1) the developed production throughput forecasting system using GRNN is able to provide forecasted results close to actual values, and to projected the future trends of production throughput in an automated hard disk drive test -330-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1464 operation; (2) IHO algorithm performed as appropriate optimization method better than the other two algorithms. (3) Compared with current forecasting system in manufacturing, the results show that the proposed system's performance is superior to the current system in prediction accuracy and suitable for real-world application. Originality/value:The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. The production throughput forecasting result is useful information for management team to respond to any changes in production processes and resources allocation. However, a practical forecasting system for production throughput has not been described in detail yet. The experiments were conducted on a real data set from the final testing operation of hard disk drive manufacturing factory by using Visual Basic Application on Microsoft Excel© to develop preliminary forecasting system for testing and verification process. The experimental result shows that the proposed model is superior to the performance of the current forecasting system.

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Non-linear variable selection for artificial neural networks using partial mutual information

Cited by 258 publications

References 43 publications

Investigating China’s Urban Air Quality Using Big Data, Information Theory, and Machine Learning

Investigating China’s Urban Air Quality Using Big Data, Information Theory, and Machine Learning

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

A production throughput forecasting system in an automated hard disk drive test operation using GRNN

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