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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021"

共 84 条记录，以下是1-10 订阅

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22nd international conference on intelligent data engineering and automated learning, ideal 2021

22nd International Conference on Intelligent Data Engineerin...

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22nd international conference on intelligent data engineering and automated learning, ideal 2021

ISBN: (纸本)9783030916077

The proceedings contain 61 papers. The special focus in this conference is on intelligent data engineering and automated learning. The topics include: Radar Echo Image Prediction Algorithm Based on Multi-scale Encoding-Decoding Network;t Line and C Line Detection and Ratio Reading of the Ovulation Test Strip Based on Deep learning;ensemble Synthetic Oversampling with Manhattan Distance for Unbalanced Hyperspectral data;indoor Positioning System for Ubiquitous Computing Environments;developments on Support Vector Machines for Multiple-Expert learning;multimodal Semi-supervised Bipolar Disorder Classification;ACHC: Associative Classifier Based on Hierarchical Clustering;meta-learning Based Feature Selection for Clustering;detecting Communities in Feature-Rich Networks with a K-Means Method;Chimera: A Hybrid Machine learning-Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis;spell Checker Application Based on Levenshtein Automaton;tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations;wind Turbine Modelling Based on Neural Networks: A First Approach;evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants;in-Car Violence Detection Based on the Audio Signal;An Implementation of the "Guess Who?" Game Using CLIP;meta-feature Extraction Strategies for Active Anomaly Detection;an intelligent Decision Support System for Production Planning in Garments Industry;evaluating Football Player Actions During Counterattacks;end-to-End Deep learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images;explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space;countering Misinformation Through Semantic-Aware Multilingual Models;neural Complexity Assessment: A Deep learning Approach to Readability Classification for European Portuguese Corpora;preface.

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Noise Tolerance and Robustness Ranking in Machine learning Models 25th

Noise Tolerance and Robustness Ranking in Machine Learning M...

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25th international conference on intelligent data engineering and automated learning

作者： Padro-Ferragut, Cristina Ramirez-Quintana, Maria Jose Martinez-Plumed, Fernando Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

Machine learning models often excel in controlled environments but may struggle with noisy, incomplete, or shifted real-world data. Ensuring that these models maintain high performance despite these imperfections is crucial for practical applications, such as medical diagnosis or autonomous driving. This paper introduces a novel framework to systematically analyse the robustness of Machine learning models against noisy data. We propose two empirical methods: (1) Noise Tolerance Estimation, which calculates the noise level a model can withstand without significant degradation in performance, and (2) Robustness Ranking, which ranks Machine learning models by their robustness at specific noise levels. Utilizing Cohen's kappa statistic, we measure the consistency between a model's predictions on original and perturbed datasets. Our methods are demonstrated using various datasets and Machine learning techniques, identifying models that maintain reliability under noisy conditions.

关键词： Model robustness Noise in data Machine learning Cohen's kappa Performanceevaluation

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Meta-learning Based Feature Selection for Clustering 22nd

Meta-Learning Based Feature Selection for Clustering

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22nd international conference on intelligent data engineering and automated learning

作者： Taratukhin, Oleg Muravyove, Sergey ITMO Univ St Petersburg Russia

ISBN: (纸本)9783030916077;9783030916084

Clustering is a highly demanded task nowadays. However, it requires the attention of human experts and it might certainly benefit from automation. This paper presents a method to perform simultaneous automatic algorithm selection and hyperparameter tuning with feature selection for clustering. The algorithm also features a meta-model to predict promising algorithm configurations based on dataset properties. Experimental results are provided for a set of benchmark datasets, it is shown that the proposed method outperforms known alternatives.

关键词： Machine learning Clustering Feature selection Meta-learning

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Neural Complexity Assessment: A Deep learning Approach to Readability Classification for European Portuguese Corpora 22nd

Neural Complexity Assessment: A Deep Learning Approach to Re...

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22nd international conference on intelligent data engineering and automated learning

作者： Correia, Joao Mendes, Rui Univ Minho Ctr Algoritmi Dept Informat Braga Portugal

ISBN: (纸本)9783030916077;9783030916084

The following paper describes a Deep learning model capable of classifying the inherent readability complexity of a piece of text in European Portuguese. The model was developed using modern Natural Language Processing techniques, featuring a highly-fine-tuned Neural Network which takes as input both the text as well as multiple metrics relating to it. This classifier was trained on a dataset featuring texts divided in 5 CEFR categories, obtaining an accuracy of 73%, a top 2 accuracy of 90% and an adjacent accuracy of 94%.

关键词： Deep learning Natural language processing Textual complexity

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Combining Encoplot and NLP Based Deep learning for Plagiarism Detection 22nd

Combining Encoplot and NLP Based Deep Learning for Plagiaris...

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22nd international conference on intelligent data engineering and automated learning

作者： Amzuloiu, Ciprian Mihaescu, Marian Cristian Rebedea, Traian Univ Craiova Craiova Romania Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9783030916077;9783030916084

This paper tackles the classical problem of plagiarism detection by employing current state-of-the-art NLP methods based on Deep learning. We investigate whether transformer models may be used along with existing solutions for plagiarism detection, such as Encoplot and clustering, to improve their results. Experimental results show that transformers represent a good solution for capturing the semantics of texts when dealing with plagiarism. Further efficiency improvements are needed as the proposed method is highly effective but also requires high computational resources. This prototype approach paves the way to further fine-tuning such that we may obtain a solution that scales well for a large number of source documents and large-sized documents.

关键词： Transformers Document embeddings Plagiarism detection Encoplot

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Finding Local Explanations Through Masking Models 22nd

Finding Local Explanations Through Masking Models

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22nd international conference on intelligent data engineering and automated learning

作者： Angiulli, Fabrizio Fassetti, Fabio Nistico, Simona Univ Calabria DIMES Dept Arcavacata Di Rende Italy

ISBN: (纸本)9783030916077;9783030916084

Among the XAI (eXplainable Artificial Intelligence) techniques, local explanations are witnessing increasing interest due to the user need to trust specific black-box decisions. In this work we explore a novel local explanation approach appliable to any kind of classifier based on generating masking models. The idea underlying the method is to learn a transformation of the input leading to a novel instance able to confuse the black-box and simultaneously minimizing dissimilarity with the instance to explain. The transformed instance then highlights the parts of the input that need to be (de-)emphasized and acts as an explanation for the local decision. We clarify differences with existing local explanation methods and experiment our approach on different image classification scenarios, pointing out advantages and peculiarities of the proposal.

关键词： eXplainable Artificial Intelligence Local explanations for machine learning Deep learning

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Multimodal Semi-supervised Bipolar Disorder Classification 22nd

Multimodal Semi-supervised Bipolar Disorder Classification

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22nd international conference on intelligent data engineering and automated learning

作者： AbaeiKoupaei, Niloufar Al Osman, Hussein Univ Ottawa Sch Elect & Comp Engn Ottawa ON Canada

ISBN: (纸本)9783030916077;9783030916084

The objective of this study is to classify the states of individuals with bipolar disorder. We employ a dataset that uses the Young Mania Recall Scale to distinguish the manic states of patients as: Mania, Hypo-Mania, and Remission. The dataset comprises audio-visual recordings of bipolar disorder patients undergoing a structured interview. Having a small dataset and confidential test labels have motivated us to train a classifier using a semi-supervised ladder network, which benefits from unlabeled data during training. The key advantage of developing a semi-supervised model is removing the manual annotation training data, which is an expensive and time-consuming. We collect informative audio, visual, and textual features from the recordings to realize a multi-model classifier of the manic states. The proposed model achieved a 53.7% UAR and 60.0% UAR on the test and development sets, respectively.

关键词： Bipolar disorder automated detection Semi-supervide learning

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Improving Maximum Likelihood Estimation Using Marginalization and Black-Box Variational Inference 22nd

Improving Maximum Likelihood Estimation Using Marginalizatio...

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22nd international conference on intelligent data engineering and automated learning

作者： Shalileh, Soroosh HSE Univ Lab Methods Big Data Anal LAMBDA Pokrovsky Blvd 11 Moscow Russia

ISBN: (纸本)9783030916077;9783030916084

Based upon Black Box Variational Inference, a new set of classification algorithms has recently emerged. The goals of this set of algorithms are twofold: 1) increasing generalization power;2) decreasing computational and implementation complexity. To this end, we assume a set of latent variables during the generation of data points. We subsequently marginalize the conventional classification likelihood objective function w.r.t this set of latent variables and then apply black-box variational inference to estimate the marginalized likelihood. We evaluate the performance of the proposed method by comparing the results obtained from the application of our method to real-world datasets with those obtained using several classification algorithms. The experimental results prove that our proposed method is competitive.

关键词： Black box variational inference classification Variational inference classification Variational inference Classification

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Detecting Communities in Feature-Rich Networks with a K-Means Method 22nd

Detecting Communities in Feature-Rich Networks with a K-Mean...

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22nd international conference on intelligent data engineering and automated learning

作者： Shalileh, Soroosh Mirkin, Boris HSE Univ Dept Data Anal & Artificial Intelligence Pokrovsky Blvd 11 Moscow Russia HSE Univ Lab Methods Big Data Anal LAMBDA Pokrovsky Blvd 11 Moscow Russia Birkbeck Univ London Dept Comp Sci & Informat Syst Malet St London WC1E 7HX England

ISBN: (纸本)9783030916077;9783030916084

The main result of this paper is an extension of the K-means algorithm to the issue of community detection in feature-rich networks. This is based on a data-recovery criterion additively combining conventional least-squares criteria for approximation of the network link data and the feature data at network nodes. The dimension of the space at which the method operates is the sum of the number of nodes and the number of features, which may be high indeed. To tackle the so-called curse of dimensionality, we replace the innate Euclidean distance with cosine distance. We experimentally validate our proposed methods and demonstrate their efficiency by comparing them to most popular approaches using both synthetic data and real-world data.

关键词： Node-attributed network Feature-rich network Cluster analysis Community detection data recovery K-means clustering

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Multi-Attribute Forecast of the Price in the Iberian Electricity Market 22nd

Multi-Attribute Forecast of the Price in the Iberian Electri...

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22nd international conference on intelligent data engineering and automated learning

作者： Peres, Goncalo Tallon-Ballesteros, Antonio J. Cavique, Luis Univ Aberta Lisbon Portugal Univ Huelva Huelva Spain

ISBN: (纸本)9783030916077;9783030916084

Electricity has been acquiring a more significant presence in our lives, and it is estimated that the future will be increasingly electric. Nowadays, we have access to enormous amounts of data that do not have much-added value if they cannot support decision-making or plan systems in advance and correctly. Forecasts are vital tools to support decision-making. We believe it is possible to resort to open data available on the Internet to make electricity price forecasts that - decision-makers can use in the sector. In this work, we study the multi-attribute hourly forecast of the electricity price in MIBEL (Iberian electricity market) for the 24 h of the following day, using open data. The realization of the multi-attribute predictions fell on the TIM ('Tangent Information Modeler') tool with AutoML ('Auto Machine learning') capabilities. The TOPSIS ('technique for order of preference by similarity to ideal solution') decision support technique was used to analyze the results.

关键词： Economic prediction Forecasting Iberian electricity market (MIBEL) Auto machine learning Multi-attribute decision

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