Guest editorial data mining and knowledge discovery with evolutionary algorithms

By A. Ghosh, A. Freitas. IEEE Transactions on Evolutionary Computation, 2003

762 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/43e58d8260b68b8dc5df6283ac8720c7bddbf8d6

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Citations in the corpus

Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems (2009, background): “Evolutionary algorithms (EAs) [14] have been used for DM with promising results [20,11].”
A Survey on Evolutionary Instance Selection and Generation (2010, methodology): “Evolutionary Algorithms have been successfully used in different data mining (Freitas, 2002; Ghosh & Jain, 2005; Abraham et al., 2006) and 32 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's…”
KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, methodology): “This makes them also a promising tool in Data Mining (DM) [8, 14 ,16,18, 35,57].”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “,is step is important inML studies [76] because the performance of a predictive model sometimes decreases when the data quality is poor [77].”
Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? (2019, background): “EAs’ powerful search in complex, ill-defined problem spaces has allowed for them to be successfully applied to a huge variety of ML and knowledge discovery tasks [10].”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “As a result, EAs tend to cope better with attribute interactions than greedy methods [10].”
A Survey on the Application of Genetic Programming to Classification (2010, background): “Rules are a simple and easily interpretable way to represent knowledge [1], [59].”
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative (2014, background): “Although a careful choice of data mining algorithms can sometimes mitigate the poor quality of the data to be mined (Fisher, Lauría, Chengalur‐ Smith, & Wang, 2006), in general, no matter how robust the data mining algorithm is, it will fail to produce accurate models if faced with ”
Fundamentals of natural computing: an overview (2007, methodology): “More specifically, evolutionary algorithms have been applied in fields like art and music composition [27,28], electronics [286], language [208], robotics [205], engineering [63], data mining and knowledge discovery [107], industry [155], signal processing [101], and many others.”
Stock Market Forecasting Research Based on Neural Network and Pattern Matching (2010, methodology): “[ 14 ] Train the Neural Networks using the selected samples, then get a model which comes closer to the stock market environment where the test samples are and a higher forecasting accuracy.”
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework (2011, background): “They have proven to be an important technique both for learning and knowledge extraction, making them a promising technique in DM [8] [16] [22] [24] [35] [46].”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “On the other hand, GAs in particular, and EAs in general, are widely used for evolving rule extraction and patterns association in DM/KD [42].”
Comprehensible classification models: a position paper (2014, background): “An explanation is that decision tree induction algorithms typically select one attribute at a time when expanding the tree, whilst rule induction algorithms typically select one attributevalue at a time when expanding a classification rule [24].”