Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study

By José Ramón Cano, Francisco Herrera, Manuel Lozano. 2003

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In Artificial Intelligence

Citations in the corpus

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power (2010, background): “PDFC [8] C = 100.0, d = 0.25, tolerance = 0.001, epsilon = 1.0EÀ12, PDRFtype = Gaussian NNEP [35] Hidden nodes = 4, Transfer = Product unit, Generations = 200 IS-CHC+1-NN [6] [20] [16] Population size = 50, Number of evaluations = 10,000, a equilibrate factor = 0.5 Percent of change in restart = 0.3”
Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability (2007, methodology): “• Evolutionary algorithms: We have selected the CHC [13] algorithm as efficient and effective model, due to its behaviour showed on [14].”
Optimal instance selection for improved decision tree (2007, background): “The objective function f(S) is taken from equation (15) and higher objective value is better.”
Stratification for scaling up evolutionary prototype selection (2005, methodology): “We introduce CHC (Eshelman, 1991) as representative and efficient EA model for PS (see Cano et al., 2003).”
A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents (2016, methodology): “KD data preprocessing data mining knowledge interpretation [29], [152], [148]”
A Novel Template Reduction Approach for the K -Nearest Neighbor Method (2009, background): “decision trees [15], 2) space partitioning [16], 3) decision boundary”
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems (2009, methodology): “In data reduction, they have been successfully used for feature selection [42,25,40,46] and instance selection [5,6,22].”