Searching for Interacting Features

By Zheng Zhao, Huan Liu. 2007

274 citations recorded by the source corpus.

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Citations in the corpus

Interpreting Classifiers through Attribute Interactions in Datasets (2017, background): “Interactions have been considered in feature selection (Zhao & Liu, 2007; 2009).”
A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, methodology): “Some of the feature selection algorithms using dependency measures have been mentioned in [52–56].”
Feature selection for high-dimensional data (2016, unknown): “INTERACT [77] Multivariate Subset Filter nm2”
Filter Methods for Feature Selection - A Comparative Study (2007, unknown): “6 (??) 100 200 150 4,6,8,11,17,19 [1-11],[13-20],38,44,100, 112,120,123,144,172,180,185,191,197,200”
A survey of feature selection and feature extraction techniques in machine learning (2014, background): “A single variable can be considered irrelevant based on its correlation to the class, but when combines with other features in the feature space, it becomes very relevant [17].”
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data (2013, background): “, Hall [29], Hall and Smith [30], Yu and Liu [68], [71], Zhao and Liu [72], [73]).”
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “INTERACT (INT) is a ﬁlter algorithm (Zhao & Liu, 2007) that searches interacting features in two steps.”
A review of feature selection methods on synthetic data (2013, methodology): “– The INTERACT algorithm [56] is a subset filter based on symmetrical uncertainty (SU) and the consistency contribution, which is an indicator about how significantly the elimination of a feature will affect consistency.”
Recent advances and emerging challenges of feature selection in the context of big data (2015, methodology): “INTERACT Multivariate Subset Zhao and Liu [30] nm2”
Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity (2008, methodology): “The complementarity effect was mentioned in several papers ([21, 18]) and has been explicitely used in feature selection algorithms only recently ([22, 28, 46]) In particular, the complementarity between two random features Xi and Xj and the output Y (n = 3)”
Multi-scale digital terrain analysis and feature selection for digital soil mapping (2010, methodology): “In contrast to the supervised ANOVA filter approach described above, which is applied on single features only, the random subspace models can reveal non-linear relationships and possible interactions between features (Zhao and Liu, 2007) when decision trees, artificial neural networks or similar met”
A peek into the black box: exploring classifiers by randomization (2014, background): “It should be noted that the task bears a close resemblance to that of finding attribute interactions when analyzing datasets in general, see e.g., Freitas (2001), Jakulin et al. (2003), Zhao and Liu (2007), Zhao and Liu (2009), Chanda et al. (2009).”
Finding Statistically Significant Attribute Interactions (2016, background): “Applications to feature selection have been studied in, e.g., [21, 22].”
A review of microarray datasets and applied feature selection methods (2014, methodology): “The INTERACT algorithm [128] uses the same goodness measure as the FCBF filter, i.”