scholarly journals Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers

2020 ◽  
Vol 34 (04) ◽  
pp. 5810-5817
Author(s):  
Masoumeh Soflaei ◽  
Hongyu Guo ◽  
Ali Al-Bashabsheh ◽  
Yongyi Mao ◽  
Richong Zhang
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Rate Distortion ◽  
Network Models ◽  
Representation Learning ◽  
Classification Problem ◽  
Neural Network Models ◽  
Rate Distortion Theory ◽  
Learning Framework ◽  
Neural Network Classifiers

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call “IB learning”. We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a “vector quantization” approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, “Aggregated Learning”, for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.

scholarly journals Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 207
Author(s):  
Asma Baccouche ◽  
Begonya Garcia-Zapirain ◽  
Cristian Castillo Olea ◽  
Adel Elmaghraby
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ensemble Learning ◽  
Heart Diseases ◽  
Hypertensive Heart Disease ◽  
Network Models ◽  
Features Selection ◽  
Neural Network Models ◽  
Learning Framework ◽  
Different Types

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

A STRUCTURAL DISTANCE-BASED CROSSOVER FOR NEURAL NETWORK CLASSIFIERS

2012 ◽  
Vol 26 (06) ◽  
pp. 1250012
Author(s):  
MANUEL MORENO ◽  
PEDRO ANTONIO GUTIÉRREZ ◽  
CÉSAR HERVÁS-MARTÍNEZ
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Crossover Operator ◽  
Classification Problems ◽  
Neural Network Models ◽  
Similarity Parameter ◽  
Fitness Evaluation ◽  
Machine Learning Classification ◽  
Structural Distance ◽  
Neural Network Classifiers

This paper presents a structural distance-based crossover for neural network classifiers, which is applied as part of a Memetic Algorithm (MA) for evolving simultaneously the structure and weights of neural network models applied to multiclass problems. Previous researchers have shown that this simultaneous evolution is a way to avoid the noisy fitness evaluation. The MA incorporates a crossover operator that shows to be useful for ameliorating the permutation problem of the network representation (i.e. different genotypes can be used to represent the same neural network phenotype), increasing the structural diversity of the individuals and improving the accuracy of the results. Instead of a recombination probability, the crossover operator considers a similarity parameter (the minimum structural distance), which allows to maintain a trade-off between global and local search. The neural network models selected in this work are the product-unit neural networks (PUNNs), due to their increasing relevance in those classification problems which show a high order relationship between the input variables. The proposed MA is intended to reduce the possible overtraining problems which can raise in some datasets for this kind of models. The evolutionary system is applied to eight classification benchmarks and the results of an analysis of variance contrast (ANOVA) show the effectiveness of the structural-based crossover operator and the capacity of our algorithm to obtain evolved PUNNs with a higher classification accuracy than those obtained using other evolutionary techniques. On the other hand, the results obtained are compared with popular effective machine learning classification methods, resulting in a competitive performance.

scholarly journals Toxic Comments Classification using Neural Network

2020 ◽  
Vol 9 (7S) ◽  
pp. 12-15
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Language Processing ◽  
Network Models ◽  
Classification Problem ◽  
The Internet ◽  
Neural Network Models ◽  
Feature Extraction Technique ◽  
Large Corpus

Humans have built broad models of expressing their thoughts via several appliances. The internet has not only become a credible method for expressing one's thoughts, but is also rapidly becoming the single largest means of doing so. In this context, one area of focus is the study of negative online behaviors of users like, toxic comments that are threat, obscenity, insults and abuse. The task of identifying and removing toxic communication from public forums is critical. The undertaking of analyzing a large corpus of comments is infeasible for human moderators. Our approach is to use Natural Language Processing (NLP) techniques to provide an efficient and accurate tool to detect online toxicity. We apply TF-IDF feature extraction technique, Neural Network models to tackle a toxic comment classification problem with a labeled dataset from Wikipedia Talk Page.

Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks

2016 ◽  
Vol 26 (05) ◽  
pp. 1650040 ◽  
Author(s):  
Francisco Javier Ropero Peláez ◽  
Mariana Antonia Aguiar-Furucho ◽  
Diego Andina
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Network Models ◽  
Activation Function ◽  
Inhibitory Interneurons ◽  
Neural Network Models ◽  
The Third ◽  
Initial Network ◽  
Sensory Cortices ◽  
Intrinsic Plasticity

In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a complex associative koniocortex network. In the initial network, intrinsic and synaptic plasticity govern the shifting of the activation function, and the modification of synaptic weights, respectively. In this first version, competition is forced, so that the most activated neuron is arbitrarily set to one and the others to zero, while in the second, competition occurs naturally due to inhibition between second layer neurons. In the third version of the network, whose architecture is similar to the koniocortex, competition also occurs naturally owing to the interplay between inhibitory interneurons and synaptic and intrinsic plasticity. A more complex associative neural network was developed based on this basic koniocortex-like neural network, capable of dealing with incomplete patterns and ideally suited to operating similarly to a learning vector quantization network. We also discuss the biological plausibility of the networks and their role in a more complex thalamocortical model.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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