Book Review: Computational Methods of Feature Selection

By Longbing Cao, David Taniar. The IEEE intelligent informatics bulletin, 2007

1,024 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7a7831d990b712492a2335cd1e18d933a22b6c8b

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In Artificial Intelligence

Citations in the corpus

Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “A filter model evaluates features without utilizing any ML algorithms by relying on the characteristics of data [26].”
A Survey on Evolutionary Instance Selection and Generation (2010, background): “234 Data reduction processes can be performed in many ways, some of the more remarkable being: • Selecting features (Liu & Motoda, 2007), reducing the number of columns in a data set.”
Feature Extraction and Selection for Emotion Recognition from EEG (2014, background): “Feature Selection methods can generally be divided into filter and wrapper methods (for reviews refer to [47], [48], [49]).”
Feature Selection for Clustering: A Review (2018, background): “Such properties pose challenges to clustering task and feature selection is an effective way to prepare high-dimensional data for effective and efficient clustering [38].”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, neutral): “Two well-known dimensionality reduction solutions are feature selection and feature extraction [68] which have attracted much attention in recent years [69].”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “Distinct from the filter methods, wrapper methods are feature selection based on three components: a search strategy, a predictor, and an evaluation function (Liu and Motoda, 2007).”
Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification (2012, background): “There is an extremely large body of work on feature selection from the machine learning community (for surveys, see [13, 22]), but we do not adopt a formal feature selection methodology here.”
Feature Selection (2016, methodology): “s(embeddedmethods).After the feature selection phase, it outputs the cluster structure of all data samples on the selected features by using a standard clustering algorithm [Guyon and Elisseeff 2003; Liu and Motoda 2007; Tang et al. 2014a]. Supervised feature selection works when sufﬁcient label inf”
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, background): “Surveys about the performance and comprehension of the FR and FS processes are presented by [15] and [16].”
Highly Comparative Feature-Based Time-Series Classification (2014, methodology): “Many more sophisticated feature selection [33–35] and classiﬁcation [38] methods exist (e.g., that allow for more robust and/or nonlinear classiﬁcation boundaries) and should improve the classiﬁcation results presented here.”
A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography (2010, methodology): “A commonly adopted remedy to the curse of dimensionality is dimensionality reduction via signal subspace estimation methods such as principal component analysis (Liu, 2008).”