Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm

Afsari, Fatemeh; Eftekhari, Mohammad; Eslami, Esfandiar; Woo, Peng-Yung

doi:10.1007/s00500-013-0981-2

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…This fusion enhances the representative power of decision trees by incorporating the knowledge component inherent in fuzzy logic, thus leading to higher robustness (noise immunity) and applicability in imprecise context (Afsari et al 2013;Hashemi and Yang 2009;Zhai 2011;Luengo et al 2012). Two main advantages of fuzzy decision tree include a high level of understanding and ability to classify (discriminate) using quantification realized in [0, 1] (Zhang et al 2014a;Zhai 2011;Khanli and Analoui 2009;Yao et al 2010;Sugumaran and Nair 2010).…”

Section: Related Workmentioning

confidence: 94%

“…Fuzzy decision trees are grown using fuzzy data and no tree transformations are performed. In this way, the fuzzy decision tree can be used efficiently (Afsari et al 2013;Hashemi and Yang 2009;Sugumaran and Nair 2010;Browne et al 2004).…”

Section: Related Workmentioning

confidence: 99%

“…Fuzzy sets and fuzzy arithmetic provide a vehicle to use linguistic and qualitative features to form rules that are more understandable and accurate (Weiqing 2005;Mitra et al 2002;Nauck 2004;Li et al 2013). The learning and generalization capabilities of decision trees by fuzzy reasoning provide possibilities to generate expressive fuzzy rules (Shah Hamzeia and Mulvaneya 1999;Afsari et al 2013). On the other hand, it is advantageous to incorporate human interaction and the domain expert's knowledge in induction rules (Weiqing 2005).…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

MFlexDT: multi flexible fuzzy decision tree for data stream classification

2015

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In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic online fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables).

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Section: Related Workmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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MFlexDT: multi flexible fuzzy decision tree for data stream classification

2015

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“…Although Pareto-optimal approach is popular in machine learning (Jin and Sendhoff 2008), it has not been explored for regression or model trees yet. However, in the literature we may find some attempts performed for classification trees (Afsari et al 2013). In Zhao (2007), the author proposes Pareto-optimal DTs to capture the trade-off between different types of misclassification errors in a cost-sensitive classification problem.…”

Section: Multi-objective Optimization and The Decision Treesmentioning

confidence: 99%

A multi-objective evolutionary approach to Pareto-optimal model trees

Czajkowski

Krȩtowski

2018

Soft Comput

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This paper discusses the multi-objective evolutionary approach to induction of model trees. The model tree is a particular case of a decision tree designed to solve regression problems. Although the decision tree induction is inherently a multiobjective task, most of the conventional learning algorithms can only deal with a single objective that may possibly aggregate multiple objectives. The goal of this paper is to demonstrate how a set of non-dominated model trees can be obtained using the Global Model Tree (GMT) system. The GMT framework can be used for the evolutionary induction of different types of decision trees, including univariate, oblique or mixed; regression and model trees. Proposed Pareto approach for GMT allows the decision maker to select desired output model according to his preferences on the conflicting objectives. Performed study covers the regression trees and the model trees with two or three objectives that relate to the tree error and the tree comprehensibility. Experimental evaluation discusses the importance of multi-objective components like crowding function and archive elitist selection, using real-life datasets. Finally, the presented multi-objective GMT solution is confronted with competitive regression and model tree inducers.

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“…Data mining approaches, like decision tree (DT), are less vulnerable to the aforesaid violations (Afsari et al 2013;Bernardo et al 2013;Ravi and Pramodh 2008). Moreover, data mining aims to identify valid, novel, potentially useful and understandable correlations and patterns in earnings management data, and can be an alternative solution to classification problems.…”

Section: Introductionmentioning

confidence: 99%

An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree

Chen

2015

Soft Comput

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This study attempts to diagnose the detecting electronic industries' earnings management by integrating suitable soft computing methods. Accounting earnings information is a very crucial element for corporate stakeholders to determine their stock prices and evaluate their supervision and management authority's performance, while it is also essential information for measuring corporate value. Hence, whether an enterprise can faithfully express its true economic meaning over its financial statements and how the management handles its earnings have turned out to be a popular issue widely discussed by researchers. Detecting public companies' earnings management is an important and challenging issue that has served as the impetus in many academic studies over the last few decades. Data mining technique and machine learning methods have also been commonly applied by accounting and financial personnel to other fields of studies. The study used the stepwise regression and random forest techniques to screen the variables in the first place, followed by adopting three kinds of decision trees including Chi-squared automatic interaction detector, classification and regression trees and C5.0 to establish a model and find out if the tested enterprise had extreme earnings manipulation. The results show that the proposed hybrid approach (RF+C5.0) has the optimal classification rate (the accuracy rate is 91.24 %) and the lowest occurrence of Type I error and Type II error. Also, as discovered from the rules set of the Communicated by V. Loia. final additional testing, an enterprise's operating cash flow, times interest earned ratio and previous period's discretionary accruals play a decisive role in affecting its extreme earnings management.

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Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm

Cited by 12 publications

References 25 publications

MFlexDT: multi flexible fuzzy decision tree for data stream classification

MFlexDT: multi flexible fuzzy decision tree for data stream classification

A multi-objective evolutionary approach to Pareto-optimal model trees

An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree

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