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Evaluating Combinations of Classification algorithms and Paragraph Vectors for News Article Classification

Evaluating Combinations of Classification Algorithms and Par...

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Federated Conference on Computer Science and Information Systems

作者： Johannes Linden Stefan Forsstrom Tingting Zhang Departement of Information Systems and Technology Mid Sweden University

ISBN: (纸本)9781538623718

News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. In this article, the focus is on the importance of evaluating critical parameter modifications of the four classification algorithms Decisiontree, Randomforest, Multi Layer perceptron and Long-Short-Term-Memory in a combination with the paragraph vector algorithms Distributed Memory and Distributed Bag of Words, with an aim to categorise news articles. The result shows that Decisiontree and Multi Layer perceptron are stable within a short interval, while Randomforest is more dependent on the parameters best split and number of trees. The most accurate model is Long-Short-Term-Memory model that achieves an accuracy of 71%.

关键词： news sorting algorithm Editors websites short interval multilayers Neural Networks (Computer) Writing Process Approach (Writing) Parameter modification jigs(templates)

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Mahalanobis Distance Metric Learning algorithm for Instance-based Data Stream Classification

Mahalanobis Distance Metric Learning Algorithm for Instance-...

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International Joint Conference on Neural Networks

作者： Jorge Luis Rivero Perez Bernardete Ribeiro Carlos Morell Perez Department of Informatics Engineering Faculty of Science and Technology University of Coimbra Department of Informatics Engineering CISUC-University of Coimbra Department of Computer Sciences Faculty of Computer Sciences Central University of Las Villas

ISBN: (纸本)9781509006212

With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this problem. A novel way to look into this issue is to take advantage of a more flexible metric due to the increased requirements imposed by the data stream scenario. In this paper we present a new algorithm that learns a Mahalanobis metric using similarity and dissimilarity constraints in an online manner. This approach hybridizes a Mahalanobis distance metric learning algorithm and a k-NN data stream classification algorithm with concept drift detection. First, some basic aspects of Mahalanobis distance metric learning are described taking into account key properties as well as online distance metric learning algorithms. Second, we implement specific evaluation methodologies and comparative metrics such as Q statistic for data stream classification algorithms. Finally, our algorithm is evaluated on different datasets by comparing its results with one of the best instance-based data stream classification algorithm of the state of the art. The results demonstrate that our proposal is better in some scenarios and has shown to be competitive in others.

关键词： data flow sorting algorithm distance metric learning algorithms Euclidean distance Evaluation Methodology

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Design of Adaptive Feature Extraction algorithm Based on Fuzzy Classifier in Hyperspectral Imagery Classification for Big Data Analysis

Design of Adaptive Feature Extraction Algorithm Based on Fuz...

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World Congress on Intelligent Control and Automation

作者： Juan F. Ramirez Rochac Nian Zhang Pradeep Behera Dept. of Electr. & Comput. Eng. Univ. of the District of Columbia Columbia WA USA Department of Electrical and Computer Engineering University of the District of Columbia 4200 Connecticut Avenue NW Washington D.C. 20008 USA Department of Civil Engineering University of the District of Columbia 4200 Connecticut Avenue NW Washington D.C. 20008 USA

ISBN: (纸本)9781467384155

We proposed a new adaptive feature extraction (FEA) approach that integrates concepts of per-pixel/field classification and spectral ummixing. It combines their advantages in adaptive feature selection while minimizing the disadvantages associated with the high-complexity of each technique. The approach consists of local gradients calculation, reference clusters determination, prototype classification using fuzzy classifier, and feature vectors selection. Multiple experiments were performed using a simulated hyperspectral cube composed by 123 samples and 1254 features and classification was done only for verification purposes. Cross-validation demonstrated that FEA generated an average improvement of 7% on the misclassification error when compared to full feature analysis.

关键词： Prototypes Hyperspectral imaging Classification algorithms Substrates Cameras Feature extraction Feature extraction hyperspectral imagery sorting algorithm Cameras PROTOTYPE Substrates Finite element method big data Classifiers Self tuning

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Multivariate Classification of Fourier Transform Infrared Hyperspectral Images of Skin Cancer Cells

Multivariate Classification of Fourier Transform Infrared Hy...

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European Signal Processing Conference

作者： Francisco Penaranda Valery Naranjo Lena Kastl Bjorn Kemper Gavin R. Lloyd Jayakrupakar Nallala Nicholas Stone Jurgen Schnekenburger Instituto de Investigacion e Innovacion en Bioingenieria (I3B) Universitat Politecnica de Valencia Biomedical Technology Center University of Munster Biophotonics Research Unit Gloucestershire Hospitals NHS Foundation Trust Biomedical Physics School of Physics University of Exeter

ISBN: (纸本)9781509018918

A multilevel framework for the multiclass classification of spectra extracted from Fourier transform infrared images is described. This learning structure was employed to discriminate the spectra extracted from hyperspectral images of two batches of four different skin cultured cells (two normal and two tumor), where the cells of one batch had been stained with fluorescence live cell dyes. Different options were explored in each stage of the framework, specifically in the spectral pre-processing and the employed classification algorithm. Special care was taken to optimize the learning models and to objectively estimate the generalization performance by means of cross-validation. A very high discriminative performance was obtained for all the unstained skin cell types. However, the presence of the stains introduces spectral artifacts that worsen the class separation, as has been demonstrated in several classification experiments.

关键词： Fourier Transform hyperspectral imagery staining cancer cell Spectra sorting algorithm Skin Neoplasms Cancer of Skin Infrared radiation Cells

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Classification of pathological disorders in children using random forest algorithm

Classification of pathological disorders in children using r...

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Emerging Trends in Information Technology and Engineering (ic-ETITE), Conference on

作者： Sujit Bebortta Manoranjan Panda Shradhanjali Panda Department of Computer Science and Engineering College of Engineering and Technology Bhubaneswar India Department of Electronics and Communication Engineering Modern Institute of Technology and Management Bhubaneswar India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

The massive technological expansions in modern healthcare solutions have substantially influenced the development of several unfolding research fields. One such interesting area involves the therapeutic prognosis of pathological conditions in patients by using knowledge engineering techniques. These techniques have inevitably assisted in the growth of healthcare systems. In this paper, we provide a comprehensive classification framework for identification of pathological disorders in children. We consider a real dataset to study the accuracy in prediction of different classification algorithms for the identification of seven different pathological conditions. We then present an experimental analysis corresponding to the performance of each algorithm. This framework can be used for the early detection of diseases and for suggesting appropriate precautionary measures. Further a more stratified understanding of the demographics of a patient's pathological conditions can be determined and appropriate treatment can be recommended.

关键词： Healthcare Machine Learning Motor Disorders Medical Informatics Pathologic Processes Medical Informatics Health Care Motor Disorders Taxonomy Artificial Intelligence Machine Learning Child sorting algorithm Health Care Systems Early Detection of Disease

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Robust Face Detection with Eyes Occluded by the Shadow from Dazzling Avoidance System

Robust Face Detection with Eyes Occluded by the Shadow from ...

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International IEEE Conference on Intelligent Transportation Systems

作者： Xiangpeng Liu Axel Graeser Inst. of Autom. Univ. of Bremen Bremen Germany

ISBN: (纸本)9781467365970

Some novel vision-based dazzling avoidance systems, such as the ShadeVision system, can intelligently cast a shadow on driver's eye region to protect the eyes from the dazzling effect that are caused by the strong external light sources. However, during this protective state (eyes covered by shadow), the system still needs to detect the driver's face to realize further monitoring, e.g., the intention of the driver as well as the dazzling effect from another light source. The protective shadow will make it difficult to apply the state-of-the-art algorithms to detect the driver's face, as the eye information is of great importance to vision-based face detection. This paper presents a series of robust algorithms for the face detection with protective shadow cast on driver's eyes, among which the Partially Masked Training, the Consecutive Sub-block Training and the Overlapped Sub-block Training are proposed for the first time for this kind of task. The on-road experimental results verify the effectiveness of the designed algorithms.

关键词： computer vision face recognition learning (artificial intelligence) object detection traffic engineering computing ShadeVision system consecutive sub-block training dazzling effect eye protection light source overlapped sub-block training partially masked training protective shadow robust face detection vision-based dazzling avoidance system vision-based face detection Classification algorithms Face Face detection Nose Skin Training Vehicles dazzling avoidance system eyes occluded by shadow face detection overlapped training Facial Recognition Eye protection shadows LIGHT SOURCES sorting algorithm Eye Object detection Drivers Face Face Nose vehicle images computer vision Functional training

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Classification with Scattering Operators

Classification with Scattering Operators

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Joan Bruna Stephane Mallat CMAP Ecole Polytechnique

ISBN: (纸本)9781457703942

A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex modulus. This scattering representation is locally translation invariant and linearizes deformations. A supervised classification algorithm is computed with a PCA model selection on scattering vectors. State of the art results are obtained for handwritten digit recognition and texture classification.

关键词： SCATTERING OPERATOR sorting algorithm broadcasting Scattering cascades state of the art COMPLEX MODULUS model selection Taxonomy

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TOWARDS MINIMIZING THE ENERGY OF SLACK VARIABLES FOR BINARY CLASSIFICATION

TOWARDS MINIMIZING THE ENERGY OF SLACK VARIABLES FOR BINARY ...

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European Signal Processing Conference

作者： Margarita Kotti Konstantinos I. Diamantaras TEI of Thessaloniki Department of Informatics

ISBN: (纸本)9781467310680

This paper presents a binary classification algorithm that is based on the minimization of the energy of slack variables, called the Mean Squared Slack (MSS). A novel kernel extension is proposed which includes the withholding of just a subset of input patterns that are misclassified during training. The later leads to a time and memory efficient system that converges in a few iterations. Two datasets are exploited for performance evaluation, namely the adult and the vertebral column dataset. Experimental results demonstrate the effectiveness of the proposed algorithm with respect to computation time and scalability. Accuracy is also high. In specific, it equals 84.951% for the adult dataset and 91.935%, for the vertebral column dataset, outperforming state-of-theart methods.

关键词： Slack minimization Kernel methods Binary classification Support vector machines Iterative solving Dataset Energy sources Binary sorting algorithm Slack variable Pulverized coal computational time spines Spine Performance Evaluation input mode Subset Converge

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Conditional Graphical Lasso for Multi-label Image Classification

Conditional Graphical Lasso for Multi-label Image Classifica...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Qiang Li Maoying Qiao Wei Bian Dacheng Tao QCIS and FEIT University of Technology Sydney

ISBN: (纸本)9781467388528

Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms.

关键词： Image classification chronic granulocytic leukemia alachlor sorting algorithm Parameter Learning Conditional Pascal image feature

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Post-Saccadic Event Related Potential toward a New Technique for Information Retrieval

Post-Saccadic Event Related Potential toward a New Technique...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Hideaki Touyama Toyama Prefectural University

ISBN: (纸本)9781467317139

In this paper, toward a brain-computer interface for information retrieval, we performed preliminary experiments on post-saccadic event related potential (ERP) during web browsing. Two channel electroencephalogram was analyzed with five subjects during simple string retrieval tasks using web browser with active eye movements, i.e., saccadic eye movements. After the saccadic eye movements the ERP signals were observed in target string retrievals, whereas not observed in standard string retrievals. The classification algorithm was applied and the average decoding performance of single-shot post-saccadic ERP could be 78.2% and 77.6% in recall and precision, respectively. The eye-tracking was also studied and the eye gaze points were confirmed to be on and around target strings when the ERP was elicited. This result suggests that the post-saccadic ERP with eye-tracking leads to a possibility of the use in the brain-computer interface for information retrieval.

关键词： Brain-Computer Interface Saccadic Eye Movement ElectroEncephaloGram (EEG) Event Related Potential (ERP) Information Retrieval Web Browsing NeuroMarketing Classification information retrieval Enterprise resource planning Brain-Computer Interfaces Event-related potentials new technique Eye Movements sorting algorithm Saccades Cosmic strings Web Browser single-shot Computer interfaces

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