Results 11 to 20 of about 774,252 (269)
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 0001 +4 more
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ACM Computing Surveys, 2017 Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean,
Jundong Li +6 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 ...
Antonio Briola, Tomaso Aste
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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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Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection
IEEE Access, 2017 Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR).
Wei Zhou +3 more
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Feature extraction for epileptic seizure detection using machine learning
Current Medicine Research and Practice, 2020 Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming.
Renuka Mohan Khati, Rajesh Ingle
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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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Metalearning for Feature Selection
CoRR, 2017 A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is provided; and a proposal regarding ...
Ben Goertzel +2 more
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Quadratic Mutual Information Feature Selection
Entropy, 2017 We propose a novel feature selection method based on quadratic mutual information which has its roots in Cauchy–Schwarz divergence and Renyi entropy. The method uses the direct estimation of quadratic mutual information from data samples using Gaussian ...
Davor Sluga, Uroš Lotrič
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