Neural Network Based Classifier Ensembles

2012 ◽  
pp. 229-239
Author(s):  
B. Verma
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classifier Ensemble ◽  
Ensemble Classifiers ◽  
Classifier Ensembles ◽  
Comparative Performance ◽  
Rotation Forest ◽  
The Third ◽  
Benchmark Datasets ◽  
Neural Classifier

This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare the author’s recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. The first type is a classifier ensemble that uses a neural network for all its base classifiers. The second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. The third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting, and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository.

scholarly journals TPOT-NN: augmenting tree-based automated machine learning with neural network estimators

2021 ◽  
Author(s):  
Joseph D. Romano ◽  
Trang T. Le ◽  
Weixuan Fu ◽  
Jason H. Moore
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Binary Classification ◽  
Inductive Learning ◽  
Future Directions ◽  
High Performing ◽  
Learning Tasks ◽  
Benchmark Datasets ◽  
Automated Machine Learning ◽  
Standard Tree

AbstractAutomated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding incredibly high-performing models to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists on when to use one versus the other. Furthermore, relatively few tools exist that allow the integration of both AutoML and ANNs in the same analysis to yield results combining both of their strengths. Here, we present TPOT-NN—a new extension to the tree-based AutoML software TPOT—and use it to explore the behavior of automated machine learning augmented with neural network estimators (AutoML+NN), particularly when compared to non-NN AutoML in the context of simple binary classification on a number of public benchmark datasets. Our observations suggest that TPOT-NN is an effective tool that achieves greater classification accuracy than standard tree-based AutoML on some datasets, with no loss in accuracy on others. We also provide preliminary guidelines for performing AutoML+NN analyses, and recommend possible future directions for AutoML+NN methods research, especially in the context of TPOT.

scholarly journals An Efficient Ensemble Learning Method for Gene Microarray Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Alireza Osareh ◽  
Bita Shadgar
Keyword(s):  
Feature Selection ◽  
Ensemble Learning ◽  
Feature Selection Method ◽  
Support Vector ◽  
Gene Microarray ◽  
Ensemble Classifiers ◽  
Classifier Ensembles ◽  
Rotation Forest ◽  
Ensemble Techniques ◽  
Effective Diagnosis

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

scholarly journals An Insight for Cursive Context-Specific Printed Script Recognition

2021 ◽  
Author(s):  
Humera Rafique ◽  
Tariq Javid
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Literary History ◽  
Learning Problems ◽  
Work Related ◽  
Benchmark Datasets ◽  
The Rich ◽  
Essential Knowledge ◽  
Context Specific

The greatest challenge of machine learning problems is to select suitable techniques and resources such as tools and datasets. Despite the existence of millions of speakers around the globe and the rich literary history of more than a thousand years, it is expensive to find the computational linguistic work related to Punjabi Shahmukhi script, a member of the Perso-Arabic context-specific script low-resource language family. This paper presents a deep insight into the related work with summary statistics, advocating the popularity and success of artificial neural networks and related techniques. The paper includes support from recent trends from the authentic sources based on the top-level researchers' feedback including the machine learning frameworks. A comprehensive comparison of the most popular deep learning techniques convolutional neural network and the recursive neural network has been presented for the cursive context-specific scripts of Perso-Arabic nature. The overview of the available benchmark datasets for machine learning problems, especially for the Perso-Arabic group, is added. This paper incorporates essential knowledge contents for the researchers in machine learning and natural language processing disciplines on the selection of algorithms, architectures, and resources.

scholarly journals Analysis of comparative performance of deep learning models for sentiment analysis

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Mirza Murtaza
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Neural Nets ◽  
Test Accuracy ◽  
Comparative Performance ◽  
Text Filtering ◽  
Percent Accuracy

Abstract Sentiment analysis of text can be performed using machine learning and natural language processing methods. However, there is no single tool or method that is effective in all cases. The objective of this research project is to determine the effectiveness of neural network-based architecture to perform sentiment analysis of customer comments and reviews, such as the ones on Amazon site. A typical sentiment analysis process involves text preparation (of acquired content), sentiment detection, sentiment classification and analysis of results. In this research, the objective is to a) identify the best approach for text preparation in a given application (text filtering approach to remove errors in data), and, most importantly, b) what is the best machine learning (feed forward neural nets, convolutional neural nets, Long Short-Term Memory networks) approach that provides best classification accuracy. In this research, a set of three thousand two hundred reviews of food related products were used to train and experiment with a neural network-based sentiment analysis system. The neural network implementation of six different models provided close to one-hundred percent accuracy of test data, and a decent test accuracy in mid-80%. The results of the research would be useful to businesses in evaluating customer preferences for products or services.  

Physical Data Auditing for Attack Detection in Cyber-Manufacturing Systems: Blockchain for Machine Learning Process

2019 ◽  
Author(s):  
Jinwoo Song ◽  
Diksha Shukla ◽  
Mingtao Wu ◽  
Vir V. Phoha ◽  
Young B. Moon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Process ◽  
Manufacturing Systems ◽  
Detection System ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Comparative Performance ◽  
Physical Data

Abstract Auditing physical data using machine learning can enhance the security in Cyber-Manufacturing System (CMS). However, the physical data processing itself is prone to cyber-attacks. Connections based on the internet in CMS opens the route for adversaries to compromise the attack detection system itself. To prevent data from malicious data injection in CMS, this paper proposes an enhanced Simple Convolutional Neural Network (SCNN) based attack detection system employing a blockchain. There are three contributions of this paper: (i) introducing a secure attack detection system using blockchain, (ii) optimizing the cost and time for CMS by training on the simulated images, and (iii) presenting a real-time attack detection system for CMS by simplifying the convolutional neural network. The paper demonstrates the effectiveness of the blockchain implementation by presenting the comparative performance analysis of the proposed attack detection system with and without blockchain implementation using an example of a simulated attack on the machine learning process.

scholarly journals Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2582
Author(s):  
Seedahmed S. Mahmoud ◽  
Akshay Kumar ◽  
Youcun Li ◽  
Yiting Tang ◽  
Qiang Fang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Healthy Individuals ◽  
Comparative Performance ◽  
Time Frequency ◽  
Assessment Tasks ◽  
Decision Logic ◽  
Performance Results ◽  
Learning Frameworks ◽  
Speech Assessment

Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev
Keyword(s):  
Neural Network ◽  
Molecular System ◽  
Computational Cost ◽  
Chemical Properties ◽  
The State ◽  
Molecular Properties ◽  
Training Data ◽  
Dft Methods ◽  
Benchmark Datasets ◽  
Quantum Phenomena

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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