Hybrid neural networks for big data classification

Hernández, Gerardo; Zamora, Erik; Sossa, Humberto; Téllez, Germán Alberto; Furlán, Federico

doi:10.1016/j.neucom.2019.08.095

Cited by 96 publications

(29 citation statements)

References 16 publications

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“…Future work can include the use of other classification methods, such as neural networks, random forest, genetic algorithms, and deep-learning or hidden-Markov models, to compare their individual or combined performance in predicting tachyarrhythmia [ 22 , 23 , 24 , 40 , 41 , 42 ]. Additionally, other indices (for instance, those derived from the non-linear analysis of HRV) could be explored.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Martinez-Alanis

Bojorges-Valdez

Wessel

et al. 2020

Sensors

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Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.

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Section: Discussionmentioning

confidence: 99%

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Martinez-Alanis

Bojorges-Valdez

Wessel

et al. 2020

Sensors

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“…Similarly, the accuracy score of the DEP classifier decreases respectively to 0.66 and 0.65 on training and test set if we invert the class labels in the double-moon classification problem. Fortunately, we can circumvent this drawback through the use of dendrite computations [37], morphological competitive units [34], or hybrid morphological/linear neural networks [41,45]. Alternatively, we can avoid the inconsistency between the partial orderings of the feature and class spaces by making use of multi-valued mathematical morphology.…”

Section: Example 5 (Double-moon)mentioning

confidence: 99%

“…Based on the ideas of Pessoa and Maragos, Araujo proposed a hybrid morphological/linear network called dilation-erosion perceptron (DEP), which is trained using a steepest descent method [40]. Steepest descent methods are also used by Hernández et al for training hybrid two-layer neural networks, where one layer is morphological and the other is linear [41]. In a similar fashion, Mondal et al proposed an hybrid morphological/linear model, called dense morphological network, which is trained using stochastic gradient descent method such as adam optimization [42].…”

Section: Introductionmentioning

confidence: 99%

Reduced Dilation-Erosion Perceptron for Binary Classification

Valle

2020

Mathematics

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Dilation and erosion are two elementary operations from mathematical morphology, a non-linear lattice computing methodology widely used for image processing and analysis. The dilation-erosion perceptron (DEP) is a morphological neural network obtained by a convex combination of a dilation and an erosion followed by the application of a hard-limiter function for binary classification tasks. A DEP classifier can be trained using a convex-concave procedure along with the minimization of the hinge loss function. As a lattice computing model, the DEP classifier assumes the feature and class spaces are partially ordered sets. In many practical situations, however, there is no natural ordering for the feature patterns. Using concepts from multi-valued mathematical morphology, this paper introduces the reduced dilation-erosion (r-DEP) classifier. An r-DEP classifier is obtained by endowing the feature space with an appropriate reduced ordering. Such reduced ordering can be determined using two approaches: One based on an ensemble of support vector classifiers (SVCs) with different kernels and the other based on a bagging of similar SVCs trained using different samples of the training set. Using several binary classification datasets from the OpenML repository, the ensemble and bagging r-DEP classifiers yielded in mean higher balanced accuracy scores than the linear, polynomial, and radial basis function (RBF) SVCs as well as their ensemble and a bagging of RBF SVCs.

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“…• It is possible that the shrinking and shifting equations can be modified for better results. • By combining LRHE with more neural layers, similar to the MNN-perceptron hybrid network concept presented by [23], it is possible that the accuracy could be further improved, although this approach will complicate the current online, discard-after-learn process of the LRHE algorithm.…”

Section: A Suggestions For Further Researchmentioning

confidence: 99%

“…Hernández et al presented hybrid neural networks, combining MNN and a classic perceptron layer [23]. By using morphological neurons as a feature-extracting hidden layer and perceptrons as the output layer, the resulting model achieved higher accuracy while also requiring fewer learning parameters to train compared to traditional models such as MLP and SVM.…”

Section: Introductionmentioning

confidence: 99%

Neural Learning With Recoil Behavior in Hyperellipsoidal Structure

Jindadoungrut¹,

Phimoltares

Lursinsap

2020

IEEE Access

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In recent years, the quantity of digital data being generated has increased considerably and is overwhelming the storage capacity. To overcome this problem, acquiring more and larger data storage is the simplest solution. But this solution is rather costly and may produce poisonous electronic garbage. A new fast and memory-efficient algorithm for learning and classifying these data without increasing the space and time complexities more than those of current learning and classifying algorithms is desirable. Although many one-pass online or incremental learning algorithms based on hyperellipsoidal functions for streaming data without retaining any learned data in fixed storage have been successfully developed for training streaming data, achieving high accuracy of any testing dataset is unstable and uncontrollable, depending on the experimental datasets. This paper proposes an improvement to these one-pass and fixed-storage learning algorithms so that the high accuracy of testing data can be significantly improved and stabilized, regardless of the experimental datasets. The concept is based on animal recoil behavior, which occurs when an animal moves away suddenly from something it dislikes. The behavior is mathematically modeled in forms of shrinking and shifting the hyperellipsoidal function during the training period to improve testing accuracy. The experimental results on 15 datasets improved the accuracy up to 8.16 % and also provided the highest or near-highest accuracy results in 10 datasets when compared to other algorithms.

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Hybrid neural networks for big data classification

Cited by 96 publications

References 16 publications

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Reduced Dilation-Erosion Perceptron for Binary Classification

Neural Learning With Recoil Behavior in Hyperellipsoidal Structure

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