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检索条件"任意字段=19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018"

共 134 条记录，以下是61-70 订阅

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Unsupervised Domain Adaptation for Human Activity Recognition 19th

Unsupervised Domain Adaptation for Human Activity Recognitio...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Barbosa, Paulo Garcia, Kemilly Dearo Mendes-Moreira, Joao de Carvalho, Andre C. P. L. F. Univ Porto Fac Engn Porto Portugal Univ Sao Paulo ICMC Sao Paulo Brazil LIAAD INESC TEC Porto Portugal Univ Twente Enschede Netherlands

ISBN: (纸本)9783030034931;9783030034924

Human Activity Recognition has been primarily investigated as a machine learning classification task forcing it to handle with two main limitations. First, it must assume that the testing data has an equal distribution with the training sample. However, the inherent structure of an activity recognition systems is fertile in distribution changes over time, for instance, a specific person can perform physical activities differently from others, and even sensors are prone to misfunction. Secondly, to model the pattern of activities carried out by each user, a significant amount of data is needed. this is impractical especially in the actual era of Big data with effortless access to public repositories. In order to deal with these problems, this paper investigates the use of Transfer learning, specifically Unsupervised Domain Adaptation, within human activity recognition systems. the yielded experiment results reveal a useful transfer of knowledge and more importantly the convenience of transfer learning within human activity recognition. Apart from the delineated experiments, our work also contributes to the field of transfer learning in general through an exhaustive survey on transfer learning for human activity recognition based on wearables.

关键词： Human activity recognition Transfer learning Unsupervised domain adaptation

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An End-to-End Human-Centered Automation of a Collaborative Analysis Platform 10

An End-to-End Human-Centered Automation of a Collaborative A...

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Joint 10th international conference on Soft Computing and intelligent Systems (SCIS) / 19th international Symposium on Advanced intelligent Systems (ISIS)

作者： Roy, Suryatapa Hu, Yaoping Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

ISBN: (纸本)9781538626337

Interactive machine learning (iML) integrates human insight into automated analysis to facilitate advanced knowledge discovery. However, state of the art in iML techniques face two significant difficulties, which hinder an end-to-end automation in such integration. One difficulty is to address different analysis requirements of individuals (i.e., users);another difficulty is to handle various experience levels of the users. these difficulties become profound in collaborative analysis, in which users work together for knowledge discovery. To overcome these difficulties, we present a novel iML scheme of adapting automated analysis workflows to user interactions. this scheme models the priority of tasks, which constitute an automated workflow. Taking account of the users' analysis objectives, the modelling incorporates implicitly their requirements based on their past and current interactions with task outputs. Moreover, the modelling exploits collective knowledge within user groups as well as document databases to map task relevance to the objectives. this human-centered modeling ensures that no explicit user intervention is necessary to adapt the workflows. Furthermore, the degree of the adaptation incorporates varying experience levels of the users, which is deduced from their interactions. thus, our iML scheme enables an end-to-end automation of analysis workflows. For a preliminary assessment of the scheme, we have designed an emulator of user interactions to generate various scenarios of collaborative analysis. We observed that the scheme exhibited desired adaptation to varying user interactions. the adaptation took a short computation time to ensure the targeted end-to-end automation. these findings show the potential of deploying the proposed fully automated iML scheme in practice.

关键词： Human-centered modeling interactive machine learning (iML) automation user interaction

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Exploring Online Novelty Detection Using First Story Detection Models 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Wang, Fei Ross, Robert J. Kelleher, John D. Dublin Inst Technol Sch Comp Dublin Ireland ADAPT Res Ctr Dublin Ireland

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the "Beast from the East" hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a clear definition of novelty. Indeed, we argue the definition of novelty is the key issue in designing a good detection model. Within the context of FSD, we first categorise online novelty detection models into three main categories, based on different definitions of novelty scores, and then compare the performances of these model categories in different features spaces. Our experimental results show that the challenge of FSD varies across novelty scores (and corresponding model categories);and, furthermore, that the detection of novelty in the very popular Word2Vec feature space is more difficult than in a normal frequency-based feature space because of a loss of word specificity.

关键词： Online novelty detection First Story Detection (FSD) Unsupervised learning Novelty score Feature space Word2Vec

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Feature Selection and Interpretable Feature Transformation: A Preliminary Study on Feature engineering for Classification Algorithms 19th

Feature Selection and Interpretable Feature Transformation: ...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Tallon-Ballesteros, Antonio J. Tuba, Milan Xue, Bing Hashimoto, Takako Univ Seville Seville Spain Singidunum Univ Belgrade Serbia Victoria Univ Wellington Wellington New Zealand Chiba Univ Commerce Konodai Ichikawa City Chiba Japan

ISBN: (纸本)9783030034962;9783030034955

this paper explores the limitation of consistency-based measures in the context of feature selection. these kinds of filters are not very widespread in large-dimensionality problems. Typically, the number of selected of attributes is very small and the ability to do right predictions is a drawback. the principal contribution of this work is the introduction of a new approach within feature engineering to create new attributes after the feature selection stage. the experimentation on multi-class problems with a feature space in the order of tens of thousands shed light on that some improvements took place with the new proposal. As a final insight, some new relationships were discovered due to the combined application of feature selection and feature transformation. Additionally, a new measure for classification problems which relates the number of features and the number of classes or labels is also proposed.

关键词： Classification Feature engineering Feature selection data mining Feature discovery Feature transformation

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A Cluster-Based Prototype Reduction for Online Classification 19th

A Cluster-Based Prototype Reduction for Online Classificatio...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Garcia, Kemilly Dearo de Carvalho, Andre C. P. L. F. Mendes-Moreira, Joao Univ Twente Enschede Netherlands Univ Sao Paulo ICMC Sao Paulo Brazil Univ Porto Fac Engn Porto Portugal LIAAD INESC TEC Porto Portugal

ISBN: (纸本)9783030034931;9783030034924

data stream is a challenging research topic in which data can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, for example, a concept drift. A concept drift occurs when the concepts associated with a dataset change when new data arrive. this paper proposes a new method based on k-Nearest Neighbors that implements a sliding window requiring less instances stored for training than existing methods. For such, a clustering approach is used to summarize data by placing labeled instances considered similar in the same cluster. Besides, instances close to the uncertainty border of existing classes are also stored, in a sliding window, to adapt the model to concept drift. the proposed method is experimentally compared with state-of-the-art classifiers from the data stream literature, regarding accuracy and processing time. According to the experimental results, the proposed method has better accuracy and less time consumption when fewer information about the concepts are stored in a single sliding window.

关键词： kNN Prototyping data stream Online clustering

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Identification of Individual Glandular Regions Using LCWT and Machine learning Techniques 19th

Identification of Individual Glandular Regions Using LCWT an...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Gabriel Garcia, Jose Colomer, Adrian Naranjo, Valery Penaranda, Francisco Sales, M. A. Univ Politecn Valencia Inst Invest & Innovac Bioingn I3B Camino Vera S-N Valencia 46022 Spain Hosp Clin Univ Valencia Serv Anat Patol Valencia Spain

ISBN: (纸本)9783030034931;9783030034924

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. the most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1.468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectively.

关键词： Machine learning Multilayer perceptron Support vector machine Locally constrained watershed transform Gland unit identification Histological prostate image

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Deep-learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Fuentes-Hurtado, Felix Morales, Sandra Mossi, Jose M. Naranjo, Valery Fedulov, Vadim Woldbye, David Klemp, Kristian Torm, Marie Larsen, Michael Univ Politecn Valencia Inst Invest & Innovac Bioingn Camino Vera S-N Valencia 46022 Spain Univ Copenhagen Dept Neurosci & Pharmacol Copenhagen Denmark Univ Copenhagen Lab Neural Plast Dept Neurosci Copenhagen Denmark Rigshosp Glostrup Dept Ophthalmol Copenhagen Denmark

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (AUC = 0.99 +/- 0.01 in a P = 15 leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. the presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. the result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

关键词： Optical coherence tomography Deep-learning Glaucoma

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Generalized Low-Computational Cost Laplacian Eigenmaps 19th

Generalized Low-Computational Cost Laplacian Eigenmaps

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Salazar-Castro, J. A. Pena, D. F. Basante, C. Ortega, C. Cruz-Cruz, L. Revelo-Fuelagan, J. Blanco-Valencia, X. P. Castellanos-Dominguez, G. Peluffo-Ordonez, D. H. Univ Nacl Colombia Sede Manizales Manizales Colombia Corp Univ Autonoma Narino Pasto Colombia Univ Narino Pasto Colombia Yachay Tech SDAS Res Grp Urcuqui Ecuador

ISBN: (纸本)9783030034931;9783030034924

Dimensionality reduction (DR) is a methodology used in many fields linked to data processing, and may represent a preprocessing stage or be an essential element for the representation and classification of data. the main objective of DR is to obtain a new representation of the original data in a space of smaller dimension, such that more refined information is produced, as well as the time of the subsequent processing is decreased and/or visual representations more intelligible for human beings are generated. the spectral DR methods involve the calculation of an eigenvalue and eigenvector decomposition, which is usually high-computational-cost demanding, and, therefore, the task of obtaining a more dynamic and interactive user-machine integration is difficult. therefore, for the design of an interactive IV system based on DR spectral methods, it is necessary to propose a strategy to reduce the computational cost required in the calculation of eigenvectors and eigenvalues. For this purpose, it is proposed to use locally linear submatrices and spectral embedding. this allows integrating natural intelligence with computational intelligence for the representation of data interactively, dynamically and at low computational cost. Additionally, an interactive model is proposed that allows the user to dynamically visualize the data through a weighted mixture.

关键词： Dimensionality reduction Generalized methodology Kernel approximations Low-computational cost Multiple kernel learning Spectral methods

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Active dataset Generation for Meta-learning System Quality Improvement 20th

Active Dataset Generation for Meta-learning System Quality I...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Zabashta, Alexey Filchenkov, Andrey ITMO Univ Machine Learning Lab 49 Kronverksky Pr St Petersburg 197101 Russia

ISBN: (纸本)9783030336073;9783030336066

Meta-learning use meta-features to formally describe datasets and find possible dependencies of algorithm performance from them. But there is not enough of various datasets to fill a meta-feature space with acceptable density for future algorithm performance prediction. To solve this problem we can use active learning. But it is required ability to generate nontrivial datasets that can help to improve the quality of the meta-learning system. In this paper we experimentally compare several such approaches based on maximize diversity and Bayesian optimization.

关键词： Machine learning Meta-learning Active learning Evolutionary Computation Optimization

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SMOTE Algorithm Variations in Balancing data Streams 20th

SMOTE Algorithm Variations in Balancing Data Streams

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Gulowaty, Bogdan Ksieniewicz, Pawel Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland

ISBN: (纸本)9783030336172;9783030336165

From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increased interest in streaming data classification field of machine learning. It is not rare, that many of those applications deal with somehow skewed or imbalanced data. In this paper, we analyze usage of smote oversampling algorithm variations in learning patterns from imbalanced data streams using different incremental learning ensemble algorithms.

关键词： data streams Imbalanced learning Synthetic oversampling Classifier ensembles

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