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检索条件"任意字段=18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017"

共 91 条记录，以下是21-30 订阅

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Relevance Metric for Counterfactuals Selection in Decision Trees 1

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

作者： Fernandez, Ruben R. Martin de Diego, Isaac Acena, Victor Moguerza, Javier M. Fernandez-Isabel, Alberto Rey Juan Carlos Univ Data Sci Lab Mostoles Spain

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

Explainable Machine learning is an emerging field in the Machine learning domain. It addresses the explicability of Machine learning models and the inherent rationale behind model predictions. In the particular case of example-based explanation methods, they are focused on using particular instances, previously defined or created, to explain the behaviour of models or predictions. Counterfactual-based explanation is one of these methods. A counterfactual is an hypothetical instance similar to an example whose explanation is of interest but with different predicted class. this paper presents a relevance metric for counterfactual selection called sGower designed to induce sparsity in Decision Trees models. It works with categorical and continuous features, while considering number of feature changes and distance between the counterfactual and the example. the proposed metric is evaluated against previous relevance metrics on several sets of categorical and continuous data, obtaining on average better results than previous approaches.

关键词： Explainable Machine learning Example-based Counterfactuals Decision Trees

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Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation 20th

Multimodal Web Based Video Annotator with Real-Time Human Po...

引用

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

作者： Rodrigues, Rui Madeira, Rui Neves Correia, Nuno Fernandes, Carla Ribeiro, Sara Polytech Inst ESTSetubal Sustain RD Ctr Setubal Portugal NOVA Univ Lisboa FCT DI NOVA LINCS Lisbon Portugal NOVA Univ Lisboa FCSH ICNOVA Lisbon Portugal

ISBN: (纸本)9783030336172;9783030336165

this paper presents a multi-platform Web-based video annotator to support multimodal annotation that can be applied to several working areas, such as dance rehearsals, among others. the CultureMoves' "Motion-Notes" Annotator was designed to assist the creative and exploratory processes of both professional and amateur users, working with a digital device for personal annotations. this prototype is being developed for any device capable of running in a modern Web browser. It is a real-time multimodal video annotator based on keyboard, touch and voice inputs. Five different ways of adding annotations have been already implemented: voice, draw, text, web URL, and mark annotations. Pose estimation functionality uses machine learning techniques to identify a person skeleton in the video frames, which gives the user another resource to identify possible annotations.

关键词： Multimodal video annotations Real-time human pose estimation Machine learning for creativity

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An Ensemble Algorithm Based on Deep learning for Tuberculosis Classification 1

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

作者： Hernandez, Alfonso Panizo, Angel Camacho, David Univ Autonoma Madrid Dept Comp Sci Madrid Spain Tech Univ Madrid Dept Informat Syst Madrid Spain

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

In the past decades the field of Artificial Intelligence, and specially the Machine learning (ML) research area, has undergone a great expansion. this has been allowed for the greater availability of data, which has not been foreign in the field of medicine. this data can be used to train supervised Machine learning algorithms. Taking into account that this data can be in form of images, several ML algorithms, such as Artificial Neural Networks, Support Vector Machines, or Deep learning Algorithms, are particularly suitable candidates to help in medical diagnosis. this works aims to study the automatic classification of X-Ray images among patients who may have tuberculosis, using an ensemble approach based on ML. In order to achieve this, an ensemble classifier, based on three pre-trained Convolutional Neural Networks, has been designed. A set of 800 samples with chest X-Ray images will be used to carry out an experimental analysis of our proposed ensemble-based classification method.

关键词： Deep learning Convolutional Neural Networks Support vector machines Image classification

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Representation learning of Knowledge Graphs with Multi-scale Capsule Network 20th

Representation Learning of Knowledge Graphs with Multi-scale...

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

作者： Cheng, Jingwei Yang, Zhi Dang, Jinming Pan, Chunguang Zhang, Fu Northeastern Univ Coll Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783030336073;9783030336066

Representation learning of knowledge graphs has gained wide attention in the field of natural language processing. Most existing knowledge representation models for knowledge graphs embed triples into a continuous low-dimensional vector space through a simple linear transformation. In spite of high computation efficiency, the fitting ability of these models is suboptimal. In this paper, we propose a multi-scale capsule network to model relations between embedding vectors from a deep perspective. We use convolution kernels with different sizes of windows in the convolutional layer inside a Capsule network to extract semantic features of entities and relations in triples. these semantic features are then represented as a continuous vector through a routing process algorithm in the capsule layer. the modulus of this vector is used as the score of confidence of correctness of a triple. Experiments show that the proposed model obtains better performance than state-of-the-art embedding models for the task of knowledge graph completion over two benchmarks, WN18RR and FB15k-237.

关键词： Representation learning Capsule network Multi-scale Dynamic routing Knowledge graph completion

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Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed data Clustering Algorithm 1

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

作者： Martarelli, Nadia Junqueira Nagano, Marcelo Seido Univ Sao Paulo Lab Appl Operat Res BR-13566590 Sao Paulo Brazil

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

the mixed data clustering algorithms have been timidly emerging since the end of the last century. One of the last algorithms proposed for this data-type has been KAMILA (KAy-means for MIxed LArge data) algorithm. While the KAMILA has outperformed the previous mixed data algorithms results, it has some gaps. Among them is the definition of numerical and categorical variable weights, which is a user-defined parameter or, by default, equal to one for all features. Hence, we propose an optimization algorithm called Biased Random-Key Genetic Algorithm for Features Weighting (BRKGAFW) to accomplish the weighting of the numerical and categorical variables in the KAMILA algorithm. the experiment relied on six real-world mixed data sets and two baseline algorithms to perform the comparison, which are the KAMILA with default weight definition, and the KAMILA with weight definition done by the traditional genetic algorithm. the results have revealed the proposed algorithm overperformed the baseline algorithms results in all data sets.

关键词： Attributes weighting Mixed data clustering Biased Random-Key Genetic Algorithm KAMILA algorithm

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A Multimodal Approach to Image Sentiment Analysis 20th

A Multimodal Approach to Image Sentiment Analysis

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

作者： Gaspar, Antonio Alexandre, Luis A. Univ Beira Interior Inst Telecomunicacoes Rua Marques d Avila e Bolama P-6201001 Covilha Portugal

ISBN: (纸本)9783030336073;9783030336066

Multimodal sentiment analysis is a process for the classification of the content of composite comments in social media at the sentiment level that takes into consideration not just the textual content but also the accompanying images. A composite comment is normally represented by the union of text and image. Multimodal sentiment analysis has a great dependency on text to obtain its classification, because image analysis can be very subjective according to the context where the image is inserted. In this paper we propose a method that reduces the text analysis dependency on this kind of classification giving more importance to the image content. Our method is divided into three main parts: a text analysis method that was adapted to the task, an image classifier tuned with the dataset that we use, and a method that analyses the class content of an image and checks the probability that it belongs to one of the possible classes. Finally a weighted sum takes the results of these methods into account to classify content according to its sentiment class. We improved the accuracy on the dataset used by more than 9%.

关键词： Multimodal sentiment analysis Image Text Deep learning

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CNNPSP: Pseudouridine Sites Prediction Based on Deep learning 20th

CNNPSP: Pseudouridine Sites Prediction Based on Deep Learnin...

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

作者： Fan, Yongxian Li, Yongzhen Yang, Huihua Pan, Xiaoyong Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Beijing Univ Posts & Telecommun Sch Automat Beijing 100876 Peoples R China

ISBN: (纸本)9783030336073;9783030336066

Pseudouridine (psi) is a kind of RNA modification, which is formed at specific site of RNA sequence due to the catalytic action of Pseudouridine synthase in the process of gene transcription. It is the most prevalent RNA modification found so far, and plays a vital role in normal biological functions. Several computational methods have been proposed to predict Pseudouridine sites, but these methods still do not achieve high accuracy. At present, deep learning has become a popular field of machine learning, and convolutional neural network (CNN) is one widely used algorithm. CNN can automatically dig into the hidden features of data and make accurate predictions, so a new algorithm based on CNN was proposed for extracting the features from RNA sequences with and without psi sites. And a predictor called CNNPSP was developed to predict psi sites in RNAs across three species (H. sapiens, S. cerevisiae and M. musculus). Both the rigorous jackknife test and independent test indicated that the new predictor outperformed the existing methods in this task.

关键词： Convolutional Neural Network Deep learning Pseudouridine sites

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Modelling Survival by Machine learning Methods in Liver Transplantation: Application to the UNOS dataset 20th

Modelling Survival by Machine Learning Methods in Liver Tran...

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

作者： Guijo-Rubio, David Villalon-Vaquero, Pedro J. Gutierrez, Pedro A. Dolores Ayllon, Maria Briceno, Javier Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci Cordoba Spain Unit Hepatobiliary Surg & Liver Transplantat Cordoba Spain

ISBN: (纸本)9783030336172;9783030336165

the aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this purpose, we consider a database from the United Network for Organ Sharing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of America from November 2004 until June 2015. the dataset contains more than a 74% of censoring, being a challenging and difficult problem. Several methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor characteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation metrics are used, being the concordance index (ipcw) the most suitable for this problem. the results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.

关键词： United Network for Organ Sharing Liver transplant Survival analysis Machine learning

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A Signature-based Assistant Random Oversampling Method for Malware Detection 18

A Signature-based Assistant Random Oversampling Method for M...

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18th IEEE international conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 13th IEEE international conference on Big data Science and engineering (IEEE BigdataSE)

作者： Pang, Ying Chen, Zhenxiang Peng, Lizhi Ma, Kun Zhao, Chuan Ji, Ke Univ Jinan Prov Key Lab Network Based Intelligent Cpmputing Jinan 250022 Peoples R China

ISBN: (纸本)9781728127767

Mobile malware detection has attracted considerable attention because people are becoming increasingly inseparable from their mobile devices. Some researchers have endeavored to discover traces of malicious apps by harnessing the semantics of network traffic. However, network traffic data naturally exhibit imbalanced distributions. To date, studies that consider such imbalanced problem have been limited. thus, we propose an oversampling method that can synthesize malicious instances based on a signature from the perspective of nominal features for mobile malware detection problem. First, we cluster the malicious network traffic datasets and generate signatures from each cluster. Second, the signatures are used to synthesize new malicious instances. the synthetic instances combined with instances obtained through the random oversampling method are used to enrich the minority class dataset. Finally, a semantic-based mobile malware detection model is built by using the rebalanced datasets. Our approach is compared with 10 other resampling algorithms on two network traffic datasets with different imbalanced ratios. Results show that the combination of the proposed method and the random oversampling algorithm has superior performance to the 10 other resampling algorithms for imbalanced malware detection.

关键词： malware detection imbalanced learning oversampling method

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the 18th Intemational conference on intelligent data engineering and automated learning（ideal＇2017） 30 Oct-1 Nov 2017,Guilin,Guangxi,China

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智能系统学报 2017年第1期12卷 132-132页

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