Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2020; 21st International C Cesar Analide,Paulo Novais,Hujun Yin Conference proc

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書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2020網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2020讀者反饋學(xué)科排名

縮影

Data Pre-processing and Data Generation in the Student Flow Case Studyon Ministry. DGEEC maintains those outcomes for each school year, therefore, this study seeks a longitudinal view based on student flow. The document reports the data pre-processing, a stochastic model based on the pre-processed data and a data generation process that uses the previous model.

Omnipotent

盤旋

Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reportside, we created a model based on a multi-layer perceptron capable of determining the approach trajectory of an aircraft thirty minutes prior to the expected landing time. Experiments on aircraft trajectories from Toulouse to Seville, show an accuracy, recall and F1-score higher than 0.9 for the resultant predictive model.

maladorit

Stabilization of Dataset Matrix Form for Classification Dataset Generation and Algorithm Selectioncation dataset aiming to break the symmetry. We experimented with it in the meta-learning problems of datasets generation and algorithm selection which were solved by conditional generative adversarial nets with convolutional networks.

Foment

Unified Performance Measure for Binary Classification Problemsmbalanced classification problems. . is compared with alternative performance measures, like the .-score or Accuracy, in both simulated confusion matrices and real datasets. The proposed measure outperforms the alternatives, providing a promising new research line.

使堅(jiān)硬

凈禮

0302-9743 ning, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. ..*. The conference was held virtually due to the COVID-19 pandemic..978-3-030-62364-7978-3-030-62365-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

符合你規(guī)定

A Preprocessing Approach for Class-Imbalanced Data Using SMOTE and Belief Function Theoryheory are then enforced to detect and remove generated instances that are in noisy and overlapping regions. Experiments on noisy artificial datasets show that our proposal significantly outperforms other popular oversampling methods.

farewell