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An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders

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BMC BIOINFORMATICS 2019年第1期20卷 1-12页

作者： Diaz Alvarez, Josefa Matias-Guiu, Jordi A. Nieves Cabrera-Martin, Maria Risco-Martin, Jose L. Ayala, Jose L. Univ Extremadura Dept Comp Architecture & Commun Merida Badajoz Spain Univ Complutense Dept Neurol Hosp Clin San Carlos San Carlos Res Hlth Inst IdISSC Madrid Spain Univ Complutense Dept Comp Architecture & Automat Madrid Spain

Background: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. Results: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. Conclusions: Although reducing the dimensionality does not means a general improvement, the set of features is almost hal

关键词： Machine learning primary progressive aphasia supervised algorithm Unsupervised algorithm Clustering Analysis

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Using a Machine Learning Logistic Regression algorithm to Classify Nanomedicine Clinical Trials in a Known Repository 1st

Using a Machine Learning Logistic Regression Algorithm to Cl...

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1st International Conference on Computer and Communication Engineering (ICCCE)

作者： Perez-Espinoza, Charles M. Beltran-Robayo, Nuvia Samaniego-Cobos, Teresa Alarcon-Salvatierra, Abel Rodriguez-Mendez, Ana Jaramillo-Barreiro, Paola Univ Agr Ecuador Guayaquil Ecuador Univ Laica Vicente Rocafuerte Guayaquil Ecuador

ISBN: (纸本)9783030120184;9783030120177

Today, the nanotechnology is the most critical technology that helps in many scientific advances, because this science allows us to work with molecular structures and their atoms, obtaining material that acts chemical and biologically different to those manifesting in bigger longitudes. Many sciences join nanotechnology to improve their researches, and one of them is medicine. In nanomedicine, many researchers are looking for a way to obtain information about these nanometric materials to enhance their studies that lead in many occasions to prove these methods or to create a new compound that helps modern medicine against dominant diseases. Years after years the world increase trials with these nanomaterials and in this work the authors are going to demonstrate this issue, using the clinical trials repository in one of the most famous trials web pages. Many scientists that work with these trials, wish to obtain only the ones those that need, but in these repositories, they have to search and read each one to make sure that the trial is about what they are researching. The authors implement an application for build a train model that involved a new method of pre-processing text to classify through logistic regression in these trials. For this classification, the authors downloaded an entire database (*** ) and used nanoinformatic with an artificial intelligence machine learning supervised algorithm to classify them. The authors classified trials that are about nanomedicine and trials that not. And finally present the results of the number of clinical trials that are about nanomedicine.

关键词： Nanotechnology Nanomedicine Nanoinformatics Machine learning Artificial intelligence supervised algorithm Text mining

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Hough Transform and Clustering for a 3-D Building Reconstruction with Tomographic SAR Point Clouds

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SENSORS 2019年第24期19卷 5378-5378页

作者： Liu, Hui Pang, Lei Li, Fang Guo, Ziye Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China

Tomographic synthetic aperture radar (TomoSAR) produces 3-D point clouds with unavoidable noise or false targets that seriously deteriorate the quality of 3-D images and the building reconstruction over urban areas. In this paper, a Hough transform was adopted to detect the outline of a building;however, on one hand, the obtained outline of a building with Hough transform is broken, and on the other hand, some of these broken lines belong to the same segment of a building outline, but the parameters of these lines are slightly different. These problems will lead to that segment of a building outline being represented by multiple different parameters in the Hough transform. Therefore, an unsupervised clustering method was employed for clustering these line parameters. The lines gathered in the same cluster were considered to correspond to a same segment of a building outline. In this way, different line parameters corresponding to a segment of a building outline were integrated into one and then the continuous outline of the building in cloud points was obtained. Steps of the proposed data processing method were as follows. First, the Hough transform was made use of to detect the lines on the tomography plane in TomoSAR point clouds. These detected lines lay on the outline of the building, but they were broken due to the density variation of point clouds. Second, the lines detected using the Hough transform were grouped as a date set for training the building outline. Unsupervised clustering was utilized to classify the lines in several clusters. The cluster number was automatically determined via the unsupervised clustering algorithm, which meant the number of straight segments of the building edge was obtained. The lines in each cluster were considered to belong to the same straight segment of the building outline. Then, within each cluster, which represents a part or a segment of the building edge, a repaired straight line was constructed. Third, between each two

关键词： TomoSAR cloud point supervised algorithm unsupervised clustering 3-D reconstruction Hough transform machine learning

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Regularized non-negative matrix factorization with Gaussian mixtures and masking model for speech enhancement

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SPEECH COMMUNICATION 2017年 87卷 18-30页

作者： Chung, Hanwook Plourde, Eric Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ Canada Univ Sherbrooke Dept Elect & Comp Engn Sherbrooke PQ Canada

We introduce single-channel supervised speech enhancement algorithms based on regularized non negative matrix factorization (NMF). In the proposed framework, the log-likelihood functions (LLF) of the magnitude spectra for both the clean speech and noise, based on Gaussian mixture models (GMM), are included as regularization terms in the NMF cost function. By using this proposed regularization as a priori information in the enhancement stage, we can exploit the statistical properties of both the clean speech and noise signals. For further improvement of the enhanced speech quality, we also incorporate a masking model of the human auditory system in our approach. Specifically, we construct a weighted Wiener filter (WWF) where the power spectral densities (PSD) of the speech and noise are estimated from the above mentioned NMF algorithm with the proposed regularization. The weighting factor in the WWF is selected based on a masking threshold which is obtained from the estimated PSD of the enhanced speech. Experimental results of perceptual evaluation of speech quality (PESQ), source-to distortion ratio (SDR) and segmental signal-to-noise ratio (SNR) show that the proposed speech enhancement algorithms (i.e., regularized NMF with and without masking model) provide better performance in speech enhancement than the benchmark algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Single-channel speech enhancement supervised algorithm Non-negative matrix factorization Regularization Gaussian mixture model Masking effects

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BASIS COMPENSATION IN NON-NEGATIVE MATRIX FACTORIZATION MODEL FOR SPEECH ENHANCEMENT 41

BASIS COMPENSATION IN NON-NEGATIVE MATRIX FACTORIZATION MODE...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chung, Hanwook Plourde, Eric Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ Canada Univ Sherbrooke Dept Elect & Comp Engn Sherbrooke PQ Canada

ISBN: (纸本)9781479999880

In this paper, we propose a basis compensation algorithm for non-negative matrix factorization (NMF) models as applied to supervised single-channel speech enhancement. In the proposed framework, we use extra free basis vectors for both the clean speech and noise during the enhancement stage in order to capture the features which are not included in the training data. Specifically, the free basis vectors of the clean speech are obtained by exploiting a priori knowledge based on a Gamma distribution. The free bases of the noise are estimated using a regularization approach, which enforces them to be orthogonal to the clean speech and noise basis vectors estimated during the training stage. Experimental results show that the proposed NMF algorithm with basis compensation provides better performance in speech enhancement than the benchmark algorithms.

关键词： Single-channel speech enhancement non-negative matrix factorization supervised algorithm basis adaptation

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BASIS COMPENSATION IN NON-NEGATIVE MATRIX FACTORIZATION MODEL FOR SPEECH ENHANCEMENT

BASIS COMPENSATION IN NON-NEGATIVE MATRIX FACTORIZATION MODE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hanwook Chung Eric Plourde Benoit Champagne Dept. of Electrical and Computer Engineering McGill University Montreal Quebec Canada Dept. of Electrical and Computer Engineering Sherbrooke University Sherbrooke Quebec Canada

ISBN: (纸本)9781479999897

关键词： Single-channel speech enhancement non-negative matrix factorization supervised algorithm basis adaptation Speech enhancement Basis Speech compensated Noise factorization NMF protocol Gamma Distribution

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Hierarchical discriminant manifold learning for dimensionality reduction and image classification

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JOURNAL OF ELECTRONIC IMAGING 2015年第5期24卷 053015-053015页

作者： Chen, Weihai Zhao, Changchen Ding, Kai Wu, Xingming Chen, Peter C. Y. Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Natl Univ Singapore Dept Mech Engn Fac Engn Singapore 119077 Singapore

In the field of image classification, it has been a trend that in order to deliver a reliable classification performance, the feature extraction model becomes increasingly more complicated, leading to a high dimensionality of image representations. This, in turn, demands greater computation resources for image classification. Thus, it is desirable to apply dimensionality reduction (DR) methods for image classification. It is necessary to apply DR methods to relieve the computational burden as well as to improve the classification accuracy. However, traditional DR methods are not compatible with modern feature extraction methods. A framework that combines manifold learning based DR and feature extraction in a deeper way for image classification is proposed. A multiscale cell representation is extracted from the spatial pyramid to satisfy the locality constraints for a manifold learning method. A spectral weighted mean filtering is proposed to eliminate noise in the feature space. A hierarchical discriminant manifold learning is proposed which incorporates both category label and image scale information to guide the DR process. Finally, the image representation is generated by concatenating dimensionality reduced cell representations from the same image. Extensive experiments are conducted to test the proposed algorithm on both scene and object recognition datasets in comparison with several well-established and state-of-the-art methods with respect to classification precision and computational time. The results verify the effectiveness of incorporating manifold learning in the feature extraction procedure and imply that the multiscale cell representations may be distributed on a manifold. (C) 2015 SPIE and IS&T

关键词： supervised algorithm cell representation manifold learning dimensionality reduction image classification

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supervised Weighting-Online Learning algorithm for Short-Term Traffic Flow Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2013年第4期14卷 1700-1707页

作者： Jeong, Young-Seon Byon, Young-Ji Castro-Neto, Manoel Mendonca Easa, Said M. Khalifa Univ Sci Technol & Res Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Khalifa Univ Sci Technol & Res Dept Civil Engn Abu Dhabi U Arab Emirates Univ Fed Ceara Dept Transportat Engn BR-60020181 Fortaleza Ceara Brazil Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada

Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.

关键词： Intelligent transportation systems (ITSs) online learning weighted support-vector regression (OLWSVR) short-term traffic flow forecast supervised algorithm

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A supervised learning computer-based algorithm to derive the amplitude of oscillations of structures using noisy GPS and Robotic Theodolites (RTS) records

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COMPUTERS & STRUCTURES 2012年 92-93卷 337-348页

作者： Psimoulis, Panos A. Stiros, Stathis C. Univ Patras Dept Civil Engn Geodesy Lab Patras 26500 Greece

A computer-based algorithm computing the mean amplitude of small-scale, high frequency oscillations of civil engineering structures using GPS and Robotic Theodolites (RTS) is presented. This algorithm is based on a peak-picking filter derived from supervised learning through independent experiments. This filter is a function of the oscillation frequency, computed even from noisy, apparent displacement records. The algorithm minimizes the noise of high-frequency GPS and RTS recordings and calculates the mean amplitude of the oscillations with millimeter accuracy. The application of this method to the Structural Health Monitoring is demonstrated in the case of measurement of displacements of two short-span bridges. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： GPS RTS Amplitude Filter supervised algorithm

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A modular supervised algorithm for vessel segmentation in red-free retinal images

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COMPUTERS IN BIOLOGY AND MEDICINE 2008年第8期38卷 913-922页

作者： Anzalone, Andrea Bizzarri, Federico Parodi, Mauro Storace, Marco Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

In this paper, a supervised algorithm for vessel segmentation in red-free images of the human retina is proposed. The algorithm is modular and made up of two fundamental blocks. The optimal values of two algorithm parameters are found out by maximizing proper measures of performances (MOPS) able to evaluate from a quantitative point of view the results provided by the proposed algorithm. The choice of the MOP allows one to tailor the solution to the specific image features to be emphasized. The performances of the algorithm are compared with those of other methods described in the literature. The simulation results show a good trade-off between quality and processing speed times. For instance, in terms of the maximum average accuracy (MAA), K value, and specificity (SP), the best performance outcomes are 0.9587, 0.8069 and 0.9477, respectively. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： optimization blood vessel segmentation retinal vasculature mapping supervised algorithm retinal imaging

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