Data Mining with Decision Trees: Theory and Applications

By Andreas Holzinger. 2015

1,205 citations recorded by the source corpus.

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “On the other hand, as anymachine learningmodel, decision trees have disadvantages [1], [2], [107], [111]; among the most important are: • An ensemble of decision trees is complicated to under-”
Decision Tree Analysis on J48 Algorithm for Data Mining (2013, neutral): “Tree size should be relatively small that can be controlled by using a technique called pruning [13].”
Recent advances in decision trees: an updated survey (2022, background): “Several great reviews have already been written on the subject, such as the ones by Murthy (1998), Rokach and Maimon (2007), Kotsiantis (2013) and Loh (2014), however, the last decade of research remains uncovered.”
Predicting ligand-dependent tumors from multi-dimensional signaling features (2017, background): “4b).(47,48) The second feature set did not contain the receptor expression explicitly but only signaling features derived from the computational model based on the receptor expression and the ligand stimulation (Fig.”
An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, neutral): “The prediction in each leaf is based on the weighted mean for node (Rokach and Maimon 2008).”
Breaking Cryptographic Implementations Using Deep Learning Techniques (2016, methodology): “A decision tree is a tool involving binary rules to classify data [45].”
Data‐Driven Materials Science: Status, Challenges, and Perspectives (2019, background): “In other cases, the input features have no apparent correlations or have completely different numerical ranges, and decision trees may exhibit the best performance.[244] The final step in the machine learning workflow is the performance assessment (cf.”
On Explaining Decision Trees (2020, neutral): “A non-exhaustivelist of references includes [43,15,54,55,59,19,33,3,12,66,50].”
Comprehensible classification models: a position paper (2014, background): “The comprehensibility of decision trees is facilitated by several factors [56], [61].”