Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution
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Citations in the corpus
- Measuring Catastrophic Forgetting in Neural Networks (2017, methodology): “In this paper, we used the Fast Correlation Based Filter (FCBF) to measure feature redundancy in each dataset (Yu and Liu 2003).”
- Feature Selection for Clustering: A Review (2018, background): “Based on different types of outputs, most feature selection algorithms fall into one of the three categories: subset selection [75], which returns a subset of selected features identified by the index of the feature; feature weighting [59], which returns weight corresponding to each feature; and the”
- Bayesian Neural Networks for Internet Traffic Classification (2007, methodology): “Following the procedure of [2], we used a Fast Correlation-Based Filter (FCBF), described in [30], as well as a variation of a wrapper method in determining the value of the threshold.”
- Gradients of Counterfactuals (2016, unknown): “Though there have been approaches to feature selection that reduce feature correlations (Yu & Liu (2003)), it is unclear how they apply to deep models on dense input.”
- Internet traffic classification using bayesian analysis techniques (2005, methodology): “In this paper, we will be using Fast Correlation-Based Filter (FCBF), described in [21], as well as a variation of a wrapper method in determining the value of the threshold (described later in this Section).”
- On Similarity Preserving Feature Selection (2013, background): “It has been known that redundant features can adversely affect the performance of classification and clustering; therefore they should be removed by feature selection for improving learning performance [7], [46].”
- Recent advances in feature selection and its applications (2017, methodology): “For example, in [65], the well-known algorithm Fast Correlation-Based Filter (FCBF) [24,25] is extended to handle multi-label data through graphical model to represent the relationships among labels and features.”
- MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles (2021, methodology): “In FCBF, the symmetrical uncertainty (SU) is calculated to measure the correlations between features by normalizing the IG values [52]: SU ( X, Y ) is in the range [0 , 1] with the value 1 indicating a perfect correlation between the two features X and Y , while the value 0 indicates the two…”
- Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing (2016, background): “The entropy of variable [22] can be defined as;”
- Character Recognition Systems: A Guide for Students and Practitioners (2007, methodology): “Typical examples of sequential search include sequential forward selection, sequential backward elimination [2, 56, 62], and branch and bound [83, 57, 81, 96]; whereas random mutation hill climbing [28, 87, 101], genetic algorithms [62, 96, 123, 115, 118, 120], simulated annealing [80], and tabu sea”
- Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, background): “The other is called filter, which selects attributes with a significance measure, independent of learning algorithms, such as distance [6], information gain [7], consistency [8], similarity [9] [10] and dependency [11].”
- Subspace clustering for high dimensional data: a review (2004, methodology): “Feature selection involves searching through various feature subsets and evaluating each of these subsets using some criterion [13; 54; 63; 76].”
- A review of microarray datasets and applied feature selection methods (2014, methodology): “The Fast Correlation-Based Filter (FCBF) method [127] is a multivariate algorithm that measures feature-class and feature-feature correlation.”