Discretization: An Enabling Technique

By Huan Liu, Farhad Hussain, Chew Lim Tan, Manoranjan Dash. 2002

1,092 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Numerical Methods & Scientific Computing

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Citations in the corpus

Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “…to increasing its sparsity, i.e., to reducing the number of active features in the classi er (Guyon et al. 2002), discretizing the features to detect active ranges of the features (Liu et al. 2002, Romero Morales and Wang 2009) or relevant thresholds for the features (Carrizosa et al. 2010).”
Supersparse linear integer models for optimized medical scoring systems (2015, background): “The values of the thresholds can be set using domain expertise, rule mining, or discretization techniques (Liu et al., 2002).”
Methods and Models for Interpretable Linear Classification (2014, background): “Such thresholds can be set using domain expertise, rule mining or discretization [69].”
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (2003, background): “Some of the recent research efforts in feature selection have been focused on these challenges from handling a huge number of instances (Liu et al., 2002b) to dealing with high dimensional data (Das, 2001; Xing et al., 2001).”
Consistency-based search in feature selection (2003, methodology): “Any continuous feature should be first discretized using some discretization method available in the literature [25].”
Comparing naive Bayes, decision trees, and SVM with AUC and accuracy (2003, background): “Discretization is also an important pre-processing step in data mining [16].”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “univariate method (Liu et al., 2002) implemented in Causal Explorer (Aliferis et al.”
Toward integrating feature selection algorithms for classification and clustering (2005, background): “discretization [28], [51] and missing value treatment, while”
Discretization Techniques: A recent survey (2006, background): “A recent overview of various types of discretization algorithms can be found in [28].”
Supervised classification and mathematical optimization (2013, background): “With the aim of reducing the computational effort, enhancing interpretability and making the classifier more robust against outliers, one can, as a preprocessing step, discretize the variables, thus reducing the number of candidate values to be checked as potential cutoffs, [147].”
An overview on subgroup discovery: foundations and applications (2011, neutral): “In this case, a previous discretisation can be applied using different mechanisms [40,88].”
Data Preprocessing for Supervised Leaning (2007, neutral): “Generally, discretization algorithms can be divided into unsupervised algorithms that discretize attributes without taking into account the class labels and supervised algorithms that discretize attributes by taking into account the classattribute [34].”
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “…methods in the last two decades to determine how continuous values should be grouped, how cut points should be positioned on the continuous scale and how many intervals should be used to generate datasets (Dougherty, Kohavi, & Sahami, 1995; Fayyad & Irani, 1993; Liu et al., 2002; Yang, 2002).”
Big data preprocessing: methods and prospects (2016, background): “The second is readability, as discrete attributes are usually easier to understand, use and explain [36].”
On Issues of Instance Selection (2002, background): “By selecting features (Blum and Langley, 1997; Liu and Motoda, 1998a), we reduce the number of columns in a data set; by discretizing feature-values (Fayyad and Irani, 1993; Bloedorn and Michalski, 1998; Hussain et al., 1999), we reduce the number of possible values of discretized features; and by s”
Bayesian Naïve Bayes classifiers to text classification (2018, methodology): “Another common technique is to use binning techniques [24,25] to discre-tise the feature values, to obtain a new set of Bernoulli-distributed features.”