Variable Selection in Nonlinear Modeling Based on RBF Networks and Evolutionary Computation

Patrinos, Panagiotis; Alexandridis, Alex; Νίνος, Κωνσταντίνος; Sarimveis, Haralambos

doi:10.1142/s0129065710002474

Cited by 33 publications

(12 citation statements)

References 85 publications

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“…22,32 GAs encode a potential solution as a chromosome-like data structure and apply genetic operations (crossover and mutation) to make the population evolve iteratively in order to reach the optimal solution. 33 At each iteration, a particular group of chromosomes (parents) are selected from the entire population to generate the offspring, which will replace chromosomes in the new population.…”

Section: Feature Selection and Classificationmentioning

confidence: 99%

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Álvarez

Alonso-Álvarez

Gutiérrez‐Tobal

et al. 2017

Journal of Clinical Sleep Medicine

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Study Objectives: Nocturnal oximetry has become known as a simple, readily available, and potentially useful diagnostic tool of childhood obstructive sleep apnea (OSA). However, at-home respiratory polygraphy (HRP) remains the preferred alternative to polysomnography (PSG) in unattended settings. The aim of this study was twofold: (1) to design and assess a novel methodology for pediatric OSA screening based on automated analysis of at-home oxyhemoglobin saturation (SpO2), and (2) to compare its diagnostic performance with HRP. Methods: SpO2 recordings were parameterized by means of time, frequency, and conventional oximetric measures. Logistic regression models were optimized using genetic algorithms (GAs) for three cutoffs for OSA: 1, 3, and 5 events/h. The diagnostic performance of logistic regression models, manual obstructive apnea-hypopnea index (OAHI) from HRP, and the conventional oxygen desaturation index ≥ 3% (ODI3) were assessed. Results: For a cutoff of 1 event/h, the optimal logistic regression model significantly outperformed both conventional HRP-derived ODI3 and OAHI: 85.5% accuracy (HRP 74.6%; ODI3 65.9%) and 0.97 area under the receiver operating characteristics curve (AUC) (HRP 0.78; ODI3 0.75) were reached. For a cutoff of 3 events/h, the logistic regression model achieved 83.4% accuracy (HRP 85.0%; ODI3 74.5%) and 0.96 AUC (HRP 0.93; ODI3 0.85) whereas using a cutoff of 5 events/h, oximetry reached 82.8% accuracy (HRP 85.1%; ODI3 76.7) and 0.97 AUC (HRP 0.95; ODI3 0.84). Conclusions: Automated analysis of at-home SpO2 recordings provide accurate detection of children with high pretest probability of OSA. Thus, unsupervised nocturnal oximetry may enable a simple and effective alternative to HRP and PSG in unattended settings. Keywords: at-home respiratory polygraphy, automated pattern recognition, blood oxygen saturation, genetic algorithms, nocturnal oximetry Citation: Álvarez D, Alonso-Álvarez ML, Gutiérrez-Tobal GC, Crespo A, Kheirandish-Gozal L, Hornero R, Gozal D, Terán-Santos J, Del Campo F. Automated screening of children with obstructive sleep apnea using nocturnal oximetry: an alternative to respiratory polygraphy in unattended settings. J Clin Sleep Med. 2017;13(5):693-702.

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Section: Feature Selection and Classificationmentioning

confidence: 99%

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Álvarez

Alonso-Álvarez

Gutiérrez‐Tobal

et al. 2017

Journal of Clinical Sleep Medicine

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“…In accordance with our earlier definition, this is used for the baseline rank score permutation of the individual feature components for the prescribed classification task and parameter settings, i.e. r b (x) = (3,1,4,2,5).…”

Section: Defining a Baseline Rankmentioning

confidence: 99%

Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Harrison

Birchall²,

Mann³

et al. 2012

Int. J. Neur. Syst.

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Feature saliency estimation and feature selection are important tasks in machine learning applications. Filters, such as distance measures are commonly used as an efficient means of estimating the saliency of individual features. However, feature rankings derived from different distance measures are frequently inconsistent. This can present reliability issues when the rankings are used for feature selection. Two novel consensus approaches to creating a more robust ranking are presented in this paper. Our experimental results show that the consensus approaches can improve reliability over a range of feature parameterizations and various seabed texture classification tasks in sidescan sonar mosaic imagery.

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“…These methods include the following: growing and pruning (Dash & Samantaray, 2004;Huang, Saratchandran, & Sundararajan, 2004;Karayiannis & Mi, 1997;Lu, Sundararajan, & Saratchandran, 1997), evolutionary optimization (Patrinos, Alexandridis, Ninos, & Sarimvei, 2010;Sarimveis, Alexandridis, & Mazarakis, 2004), and multiple linear regression (MLR), i.e., least squares regression (LSR), partial least squares regression (PLSR) (Walczak & Massart, 1996), and principal component analysis (PCA) (Weizhen et al, 2004;Xie, Ye, Liu, & Ying, n.d.). The pruning approach is the most widely used in the literature because it can train large and small networks.…”

Section: Introductionmentioning

confidence: 99%

Modified Radial Basis Function Neural Network Integrated with Multiple Regression Analysis and its Application in the Chemical Industry Processes

Wang

Chen

Yan

2013

Intelligent Automation & Soft Computing

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The construct of a radial basis function neural network (RBFNN) plays an important role in predicting performance. However, determining the optimal construct is difficult. A modified RBFNN integrated with correlation pruning algorithm-least squares regression (CPA-LSR) was proposed to optimize the number of hidden neurons as well as the weights and bias. First, an initial RBFNN was built by superposing each center to a training set point. This RBFNN was then trained. Next, CPA-LSR was applied to eliminate the redundant information of the initial network and to improve the predicting performance by optimizing the structure as well as the weights and bias. Finally, the developed naphtha dry point soft sensor and the industrial oxidation of p-xylene to terephthalic acid were employed to illustrate the performances of the modified RBFNNs. The result reveals an improvement in the predicting performances of the RBFNNs integrated with CPA-LSR. Conclusively, RBFNNs integrated with CPA-LSR are recommended because the redundant neurons are effectively eliminated, and the optimal structure of the RBFNN is obtained by CPA-LSR. Moreover, such RBFNNs are intuitive and eliminate the need for parameterization.

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Variable Selection in Nonlinear Modeling Based on RBF Networks and Evolutionary Computation

Cited by 33 publications

References 85 publications

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Modified Radial Basis Function Neural Network Integrated with Multiple Regression Analysis and its Application in the Chemical Industry Processes

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