Consistency-based search in feature selection

By Manoranjan Dash, Huan Liu. 2003

931 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Adversarial Feature Selection Against Evasion Attacks (2016, methodology): “Feature selection methods can be divided into three categories according to their interaction with classification algorithms [38], [44], [52].”
Feature selection based on artificial bee colony and gradient boosting decision tree (2019, background): “[16] presented a general framework of feature selection that includes four phases: generating a subset of features according to rules, the candidate subset is evaluated, repeating the subset generation if the stop rule is not satisfied, validate the final subset of features.”
Normalized Mutual Information Feature Selection (2009, neutral): “Several different criteria have been used for evaluating the goodness of a feature [4] including distance measures [5], [6], dependency measures [7], [8], consistency measures [9], [10], information measures [11], [12], and classification error measures [13].”
Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, methodology): “To balance the tradeoff of result optimality and computational efﬁciency, different search strategies such as complete, heuristic, and random search have been studied to generate candidate feature subsets for evaluation (Blum and Langley, 1997; Dash and Liu, 2003).”
A rough set approach to feature selection based on ant colony optimization (2010, background): “: +86 21 69589359. yon et al., 2002; Neumann et al., 2005; Gasca et al., 2006; Xie et al., 2006) and classifier independent (Kira and Rendell, 1992; Modrzejewski, 1993; Dash and Liu, 2003).”
Positive approximation: An accelerator for attribute reduction in rough set theory (2010, methodology): “Tens, hundreds, and even thousands of attributes are stored in many real-world application databases [6,12,37].”
Character Recognition Systems: A Guide for Students and Practitioners (2007, methodology): “Marr [22] describes any type of significant scene structures in an image as discontinuities”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “Feature selection has been shown to be highly useful in reducing dimensionality while preserving the underlying meaning of the features involved [69,214].”
Searching for interacting features in subset selection (2009, neutral): “…mainly based on their individually high correlations with the class, and some relevant interacting features of high order may be removed [20,38,39] because the irreducible nature of feature interaction cannot be attained by SFS. Revisiting the MONK1 problem, a feature subset selection…”
Feature Selection With Harmony Search (2012, methodology): “[58] and fuzzy-rough sets [3], [24], [25], [51], probabilistic consistency [8], [34], and correlation analysis [19], [20].”
Genetic programming for simultaneous feature selection and classifier design (2006, background): “Alternatively, they can also be categorized [1] into five groups based on the evaluation function that is used for evaluating the utility of the features: distance [2], [3], information [4], [5], dependence [6], consistency [7], [8] and classifier error rate.”
Feature selection for high-dimensional data (2016, neutral): “Consistency [20] Multivariate Subset Filter nm2”
A hybrid genetic algorithm for feature selection wrapper based on mutual information (2007, methodology): “The consistency measure is heavily relied on the training data set and discussed for feature selection in (Dash and Liu, 2003).”
Neighborhood rough set based heterogeneous feature subset selection (2008, methodology): “The monotonicity of the dependency function is very important for constructing a greedy forward or backward search algorithm [4].”