Discretization Techniques: A recent survey

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A naive Bayes probability estimation model based on self-adaptive differential evolution (2014, methodology): “In our experiments, the equal width discretization (EWD) approach (Kotsiantis and Kanellopoulos 2006) is employed to determine the minimum and maximum values of the discretized attribute and then divide the range into the user-defined number of equal width discrete intervals.”
Feature Selection (2016, methodology): “For numeric feature values, some data discretization techniques (Dougherty et al., 1995; Kotsiantis and Kanellopoulos, 2006) are required.”
How Complex Is Your Classification Problem? (2018, methodology): “Using this solution, continuous features must be discretized a priori, which imposes the difficulty of choosing a proper discretization technique and associated parameters, an open issue in data mining [Kotsiantis and Kanellopoulos, 2006].”