Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis

Yuan, Chao; Wang, Xue Zhong

doi:10.1016/j.compchemeng.2009.04.005

Cited by 24 publications

(26 citation statements)

References 43 publications

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“…This crossover selects nodes in two individuals and exchanges the entire subtrees corresponding to each selected node, generating two offspring. This operator is used in [28], [35]- [45], [51], [55]- [58], [61]- [67], [69], [73], [77]- [82], [85]- [88]. Two small variations of this strategy are found in the literature.…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

“…A small number of works, however, argue that a multi-objective approach that seeks a compromise between predictive accuracy and solution complexity (tree size) as a form of parsimony pressure is not as beneficial as it may sound. Ma and Wang [67], for instance, argue that this compromise reduces the search space and leads to a slower overall increase in accuracy. Moreover, they reckon that the search may get stuck in regions containing less accurate trees of the same size as those produced without using the complexity penalty in the multi-objective fitness function, and that, as a result, the parsimony pressure would be a disadvantage instead of an advantage.…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

“…This is the most rudimentary use of task-dependent knowledge in the initialization process. For instance, Ma and Wang [67] reduce the impact of randomness by backtracking a "bad" random choice and replacing it by a "better" random choice. The definition of good and bad choices, in this case, is given by the amount of training data that are filtered to each child node of the currently generated node.…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

“…Usually no further details are given on how the new subtree is randomly generated. This approach is used in [38], [51], [56]- [58], [61]- [64], [67], [79], [81], [82], [85], [86]. In a more restricted version of this approach a subtree is replaced by a random leaf node or a leaf node is replaced by a random subtree [28], [45], [54], [64], [69]- [71], [77], [78], [80], [82].…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

“…In evolutionary induction of decision trees, the most frequently used approach for selection is tournament selection.This selection method is used in [28], [38], [45], [48], [51], [52], [56]- [59], [64], [66], [67], [75], [76], [85].…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Barros

Basgalupp

Carvalho

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

284

139

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Abstract-This paper presents a survey of evolutionary algorithms designed for decision tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divideand-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision tree classifiers. The paper original contributions are the following. First, it provides an upto-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy which addresses works that evolve decision trees and works that design decision tree components using evolutionary algorithms. Finally, a number of references is provided that describe applications of evolutionary algorithms for decision tree induction in different domains. The paper ends by addressing some important issues and open questions that can be subject of future research.

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Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Barros

Basgalupp

Carvalho

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

284

139

View full text Add to dashboard Cite

show abstract

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

et al. 2021

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Fault detection and diagnosis for process plants has been an active area of research for many years. This review presents a concise overview on supervised and unsupervised data‐driven approaches for fault detection and diagnosis in chemical processes. Methods based on supervised and unsupervised data‐driven techniques are reviewed, and the challenges in the field of fault detection and diagnosis have also been highlighted. It is observed that most of the data‐driven approaches are application specific, in that no single method can be used to obtain a generalized solution for nearly all purposes. The methods reviewed differ significantly from one to the other, and hence it is difficult to generalize any key similarity. The majority of the works proposed in the literature focused mainly on single fault detection, and do not cover the root‐cause diagnosis of the detected faults. In cases where both detection and diagnosis are performed, the focus is mainly for a single fault. In addition, majority of the articles do not extend to the diagnosis of the root cause for multiple and simultaneous faults.

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Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

Oksel

Cai

Liu

et al. 2017

Advances in Experimental Medicine and Biology

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Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

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Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis

Cited by 24 publications

References 43 publications

A Survey of Evolutionary Algorithms for Decision-Tree Induction

A Survey of Evolutionary Algorithms for Decision-Tree Induction

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

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