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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7120 条记录，以下是51-60 订阅

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A Deep-learning Approach for the Identification of New Subtypes of Lung Cancer 25th

A Deep-Learning Approach for the Identification of New Subty...

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25th international conference on intelligent data engineering and automated learning

作者： Banerjee, Tuhin Corradini, Andrea Canon EMEA Amstelveen Netherlands MCI Innsbruck Austria

ISBN: (纸本)9783031777301;9783031777318

Lung cancer is the deadliest cancer in the world. It is caused by unchecked cell division of damaged cells in the lungs forming tumors that eventually prevent the lung from functioning properly. Identification of novel unsupervised subtypes of lung cancer is critical to reveal new insights into the underlying biology of cancer and ensure that patients receive specialized precision treatment based on the subtype of cancer they are suffering from. the ability of modern sequencing tools to produce patient-specific RNA sequencing (RNA-seq) gene expression data has transformed cancer research by offering in-depth understanding of the molecular landscape of cancer. this paper reports on a pipeline that comprise of a Deep Autoencoder (DAE) model coupled with hierarchical agglomerative clustering (H-Clust). It aims to identify new unsupervised lung cancer subtypes from RNA-seq expression samples collected from a publicly available dataset. Further, a deep learning (DL) model, Artificial Neural Network (ANN) is used to classify a patient's data into one of the newly identified subtypes.

关键词： lung cancer subtype TCGA RNA-seq deep learning DAE encoder-decoder agglomerative clustering ANN

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MetaLIRS: Meta-learning for Imputation and Regression Selection 25th

MetaLIRS: Meta-learning for Imputation and Regression Select...

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25th international conference on intelligent data engineering and automated learning

作者： Erez, Ill Baysal Flokstra, Jan Pod, Mannes van Keulen, Maurice Univ Twente EEMCS NL-7500 AE Enschede Netherlands

ISBN: (纸本)9783031777301;9783031777318

Missing data is a prevalent problem in data science for many fields such as natural, social, and health sciences. Since most regression methods can not handle missing data directly, imputation methods are used in data pre-processing. Finding the best imputation method is non-trivial, however. Moreover, our results show that an independent choice for a best imputation method does not always result in the best predictive performance in the end;the combination matters. Furthermore, search-based approaches for finding a best-fitting imputer/regressor-pair can be computationally intensive. In this paper, we propose the MetaLIRS (Meta learning Imputation and Regression Selection) framework for developing resource-friendly ML-based recommendation models for method selection. With MetaLIRS, we constructed a proof-of-concept recommendation model based on 12 meta-features that achieves an accuracy of 63% for selecting the best-fitting imputer/regressor-pair. A data scientist can use this model for a quick resource-friendly recommendation on which imputation and regression method to use for their particular data set and task without the need for an expensive grid search among methods.

关键词： Missing data Imputation Regression

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Predicting Employee Attrition in a Multi-company Setting 25th

Predicting Employee Attrition in a Multi-company Setting

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25th international conference on intelligent data engineering and automated learning

作者： Gomes, Adriano Silva, Luis M. Cruz, Joao Pedro Univ Aveiro Dept Econ Management Ind Engn & Tourism P-3810193 Aveiro Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Dept Math P-3810193 Aveiro Portugal

ISBN: (纸本)9783031777301;9783031777318

this paper describes the creation of a database and a machine learning model to predict employee attrition. Our proposal deals with attrition by considering 3 classes (voluntary, involuntary and no attritors) giving a more complete view of the loss of qualified personnel to the Human Resources Management. Of the several machine learning models tested to solve the problem, XGBoost stood out as the best performing one on a dataset with more than four thousand employees and twenty-one features collected from three independent companies from different industrial sectors. the model, evaluated on a 20-run experiment, achieved an overall mean accuracy of 78.5%, corresponding to the correct classification of 52.6% of the voluntary attritors, 78.9% of the involuntary attritors and 81.6% of the non-attritors, showing that voluntary attritors are harder to discriminate.

关键词： Employee attrition Machine learning Real data Human resources management Industry 4.0

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A Comprehensive Digital Solution for Identifying and Addressing Academic Risk in Middle Education 25th

A Comprehensive Digital Solution for Identifying and Address...

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25th international conference on intelligent data engineering and automated learning

作者： Magalhaes, Renata Duraes, Dalila Costa, Antonio Machado, Jose Novais, Paulo Univ Minho Algoritmi Ctr LASI Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

Smart schooling seeks to enhance the educational experience through technology. In this effort, a digital educational platform has been developed and empirically tested to identify students at risk of academic failure and dropout, while also promoting effective study and learning habits. Machine learning algorithms are employed to assess academic failure risk based on students' responses to a questionnaire created by educational psychologists. the platform predicts students' academic performance by analyzing factors related to their school and home environments, as well as their motivation to learn. Additionally, a decision support system is integrated to alert the class director and recommend preventive actions when risks or unusual behaviors are identified. A decision support system is incorporated to alert the class director and suggest preventive measures after risk or unusual behaviour is detected.

关键词： academic failure conversational agent smart education machine learning decision support system

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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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data Analysis and Anomaly Detection in a Wind Farm with k-Nearest Neighbors 25th

Data Analysis and Anomaly Detection in a Wind Farm with k-Ne...

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25th international conference on intelligent data engineering and automated learning

作者： Weiss, Bassel Esteban, Segundo Santos, Matilde Univ Complutense Madrid Dpto Arquitectura Computadores Automat Madrid 28040 Spain UCM Inst Knowledge Technol Madrid 28040 Spain

ISBN: (纸本)9783031777370;9783031777387

this paper presents an in-depth analysis of data from the Alpha Ventus offshore wind farm, emphasizing the identification and detection of anomalies in wind turbine performance. Utilizing real-world data from the RAVE (Research at Alpha Ventus) project, we explore the complexities of offshore wind energy generation, including the effects of wind speed, nacelle position, and environmental factors on turbine behaviour. In this paper, among the various machine learning techniques, we have selected k-nearest neighbours (k-NN), to identify patterns and detect anomalies indicative of potential issues. Our findings demonstrate that some turbines of the wind farm, centrally located, are subject to significant wake effects and operational irregularities. By adjusting the parameters of the k-NN model, we achieved an anomaly detection framework, enhancing the reliability of turbine operation and maintenance.

关键词： Offshore wind energy Anomaly detection Wind turbine data analytics Machine learning K-nearest neighbours Predictive maintenance

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Federated learning with Discriminative Naive Bayes Classifier 25th

Federated Learning with Discriminative Naive Bayes Classifie...

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25th international conference on intelligent data engineering and automated learning

作者： Torrijos, Pablo Alfaro, Juan C. Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informaticos Albacete 02071 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. this paper proposes a new federated approach for Naive Bayes (NB) classification, assuming discrete variables. Our approach federates a discriminative variant of NB, sharing meaningless parameters instead of conditional probability tables. therefore, this process is more reliable against possible attacks. We conduct extensive experiments on 12 datasets to validate the efficacy of our approach, comparing federated and non-federated settings. Additionally, we benchmark our method against the generative variant of NB, which serves as a baseline for comparison. Our experimental results demonstrate the effectiveness of our method in achieving accurate classification.

关键词： Federated learning Bayesian network classifiers Naive Bayes Discriminative learning

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Using data Augmentation for Improving Text Summarization 25th

Using Data Augmentation for Improving Text Summarization

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25th international conference on intelligent data engineering and automated learning

作者： Constantin, Daniel Mihaescu, Marian Cristian Heras, Stella Jordan, Jaume Palanca, Javier Julian, Vicente Univ Craiova Craiova Romania VRAIN Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

In today's society, the amount of information we need to process daily from sources such as news, videos, and literature is relatively high. the primary strategy to decrease the workload is to use effective summarization techniques, either through extractive (where the summary is made up of extracts from the source itself) or abstractive methods. Traditional summarization models often rely on extensive humanannotated data, which is usually quite costly. this research proposes an approach leveraging transformer models to optimize and affordably augment small datasets, enhancing the performance of summarization models. Using sentence clustering and pre-trained models on tasks such as summarization or paraphrasing, we explore whether such an approach can yield better results across various summarization datasets that target different formats, such as video conference transcripts and news articles.

关键词： Text Summarization data Augmentation Transformer Models ROUGE

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Assessing the Impact of Temporal data Aggregation on the Reliability of Predictive Machine learning Models 25th

Assessing the Impact of Temporal Data Aggregation on the Rel...

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25th international conference on intelligent data engineering and automated learning

作者： Barhrhouj, Ayah Ananou, Bouchra Ouladsine, Mustapha Aix Marseille Univ Univ Toulon CNRS LIS UMR 7020 Marseille France

ISBN: (纸本)9783031777301;9783031777318

In time series analysis, data aggregation is an essential preprocessing step that consolidates data points over specified time intervals, simplifying the data structure and reducing noise. this process is vital for enhancing the manageability of large, complex datasets, commonly encountered in various domains such as energy consumption forecasting. However, the choice of the most appropriate temporal aggregation (TA) frequency can significantly impact the performance and the reliability of predictive models. Different TA frequencies may perform better in different scenarios, and there is a lack of evidence on what indicators may determine the superiority of each one. In this study, we design and execute an empirical experiment framework to first explore the performance of various machine learning (ML) models using different TA frequencies on 04 multivariate time series datasets of ship fuel consumption. We further investigate the reliability of those models by studying the marginal contribution of the time series features in each aggregation period. Using the Random Forest Regressor, XGBoost Regressor, LightGBM, and Extra Trees predictive models along with the Shapley Additive Explanations to analyze feature contributions, we found that time series aggregation consistently produces accurate results regardless of the TA period. However, the models may exhibit significant biases, highlighting the need to examine their inner workings to select the appropriate TA period.

关键词： Machine learning Explainable AI Time-series Temporal aggregation

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Counterfactual Explanations for Sustainable Tourism Indicators 25th

Counterfactual Explanations for Sustainable Tourism Indicato...

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25th international conference on intelligent data engineering and automated learning

作者： Saugar, Javier Lancho, Carmen Cuesta, Marina Cano, Emilio L. Martin de Diego, Isaac Amado, Antonio Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain Dephimatica SL C Rios Rosas 44 3 C Madrid 28002 Spain

ISBN: (纸本)9783031777301;9783031777318

Counterfactual explanations are a well-known technique in Explainable Machine learning (XML) to provide simple explanations on complex Machine learning (ML) models. through understandable "what if" scenarios, counterfactuals explore how changes in the input data affect the results of a model. this article leverages counterfactual explanations for sustainable tourism, an emerging approach within the tourism industry to mitigate the negative impacts of mass tourism on ecological systems and local communities. the proposed method analyzes the relationships between several Sustainable Tourism Indicators (STIs) defined for a specific tourist destination and its general sustainability assessment. It identifies the key changes needed in the STIs to achieve an improved global sustainability score. As a result, a decision-making system is offered for sustainable tourism management, which domain experts can use to make more informed decisions. the effectiveness of the proposed method is illustrated through its application to Mallorca, a popular Spanish tourist destination.

关键词： Counterfactuals Explainable Machine learning Explainable Artificial Intelligence Sustainable Tourism

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