Results 11 to 20 of about 642,813 (313)
Agnostic Feature Selection [PDF]
, 2020 Unsupervised feature selection is mostly assessed along a supervised learning setting, depending on whether the selected features efficiently permit to predict the (unknown) target variable. Another setting is proposed in this paper: the selected features aim to efficiently recover the whole dataset.
Doquet, Guillaume Florent +1 more
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Online Feature Selection with Streaming Features [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed.
Xindong Wu +4 more
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, 2023 In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal graphs (the Triangulated Maximally Filtered Graph), and we maximise the likelihood of features' relevance ...
Briola, Antonio, Aste, Tomaso
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International Journal of Islamic Business and Economics (IJIBEC), 2017 Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe.
Ivandari Ivandari +3 more
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New Feature Selection Algorithm Based on Feature Stability and Correlation
IEEE Access, 2022 The analysis of a large amount of data with high dimensionality of rows and columns increases the load of machine learning algorithms. Such data are likely to have noise and consequently, obstruct the performance of machine learning algorithms.
Luai Al-Shalabi
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Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection
Entropy, 2016 Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings.
Jaesung Lee, Dae-Won Kim
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Dynamic Feature Selection for Clustering High Dimensional Data Streams
IEEE Access, 2019 Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This
Conor Fahy, Shengxiang Yang
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Feature Selection Embedded Robust K-Means
IEEE Access, 2020 Clustering is one of the most important unsupervised learning problems in machine learning. As one of the most widely used clustering algorithms, K-means has been studied extensively.
Qian Zhang, Chong Peng
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Feature selection on quantum computers
Quantum Machine Intelligence, 2023 AbstractIn machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of ...
Sascha Mücke +4 more
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