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Machine Learning
Scopus
ISSN 0885-6125 · Springer Science+Business Media · 9 artikel di indeks
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Artikel Jurnal Teks lengkap
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Abstract Class imbalance occurs when the class distribution is not equal. Namely, one class is under-represented (minority class), and the other class has significantly more samples in the data (majority class). The class imbalance problem is prevalent in many real world applications. Generally…
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Artikel Jurnal Teks lengkap
HIVE-COTE 2.0: a new meta ensemble for time series classification
Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and…
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Artikel Jurnal Teks lengkap
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics
Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been used to automate this process. During experiments on…
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Artikel Jurnal Teks lengkap
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Abstract The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet…
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Artikel Jurnal Teks lengkap
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
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Artikel Jurnal Teks lengkap
F*: an interpretable transformation of the F-measure
Abstract The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as…
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Artikel Jurnal Teks lengkap
Evaluating time series forecasting models: an empirical study on performance estimation methods