Data Mining Using Fuzzy Decision Trees

2010 ◽  
pp. 47-66 ◽  
Author(s):  
Malcolm J. Beynon ◽  
Martin Kitchener
Keyword(s):  
Data Mining ◽  
Decision Trees ◽  
State Government ◽  
Fuzzy Set ◽  
Long Term Care ◽  
General Strategy ◽  
Fuzzy Decision ◽  
Term Care ◽  
Fuzzy Decision Trees

The chapter exposits the strategies employed by the public long-term care systems operated by each U.S. state government. The central technique employed in this investigation is fuzzy decision trees (FDTs), producing a rule-based classification system using the well known soft computing methodology of fuzzy set theory. It is a timely exposition, with the employment of set-theoretic approaches to organizational configurations, including the fuzzy set representation, starting to be discussed. The survey details considered, asked respondents to assign each state system to one of the three ‘orientations to innovation’ contained within Miles and Snows’ (1978) classic typology of organizational strategies. The instigated aggregation of the experts’ opinions adheres to the fact that each long-term care system, like all organizations, is “likely to be part prospector, part defender, and part reactor, reflecting the complexity of organizational strategy”. The use of FDTs in the considered organization research problem is pertinent since the linguistic based fuzzy decision rules constructed, open up the ability to understand the relationship between a state’s attributes and their predicted position in a general strategy domain - the essence of data mining.

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 1175-1180
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Trees ◽  
Decision Rule ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Decision ◽  
Fuzzy Decision Trees ◽  
Rule Systems

The seminal work of Zadeh (1965), fuzzy set theory (FST) has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (and prediction), (see Breiman 2001).

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 2024-2030
Author(s):  
Malcolm J. Beynonm
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Complex Decision Making ◽  
Fuzzy Decision Trees

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.

scholarly journals Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4905
Author(s):  
Bartłomiej Gaweł ◽  
Andrzej Paliński
Keyword(s):  
Natural Gas ◽  
Decision Trees ◽  
Energy Intensity ◽  
Fuzzy Decision ◽  
Energy Mix ◽  
Starting Point ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Fuzzy Decision Trees

Classic forecasting methods of natural gas consumption extrapolate trends from the past to subsequent periods of time. The paper presents a different approach that uses analogues to create long-term forecasts of the annual natural gas consumption. The energy intensity (energy consumption per dollar of Gross Domestic Product—GDP) and gas share in energy mix in some countries, usually more developed, are the starting point for forecasts of other countries in the later period. The novelty of the approach arises in the use of cluster analysis to create similar groups of countries and periods based on two indicators: energy intensity of GDP and share of natural gas consumption in the energy mix, and then the use of fuzzy decision trees for classifying countries in different years into clusters based on several other economic indicators. The final long-term forecasts are obtained with the use of fuzzy decision trees by combining the forecasts for different fuzzy sets made by the method of relative chain increments. The forecast accuracy of our method is higher than that of other benchmark methods. The proposed method may be an excellent tool for forecasting long-term territorial natural gas consumption for any administrative unit.

scholarly journals Long-Term Care and Gender Equality: Fuzzy-Set Ideal Types of Care Regimes in Europe

2020 ◽  
Vol 8 (4) ◽  
pp. 92-102 ◽  
Author(s):  
Attila Bartha ◽  
Violetta Zentai
Keyword(s):  
Gender Equality ◽  
Fuzzy Set ◽  
Long Term Care ◽  
Ideal Type ◽  
Term Care ◽  
Ideal Types ◽  
Intergenerational Ties ◽  
And Gender ◽  
Care Regimes

Recent changes in the organization of long-term care have had controversial effects on gender inequality in Europe. In response to the challenges of ageing populations, almost all countries have adopted reform measures to secure the increasing resource needs for care, to ensure care services by different providers, to regulate the quality of services, and overall to recalibrate the work-life balance for men and women. These reforms are embedded in different family ideals of intergenerational ties and dependencies, divisions of responsibilities between state, market, family, and community actors, and backed by wider societal support to families to care for their elderly and disabled members. This article disentangles the different components of the notion of ‘(de)familialization’ which has become a crucial concept of care scholarship. We use a fuzzy-set ideal type analysis to investigate care policies and work-family reconciliation policies shaping long-term care regimes. We are making steps to reveal aggregate gender equality impacts of intermingling policy dynamics and also to relate the analysis to migrant care work effects. The results are explained in a four-pronged ideal type scheme to which European countries belong. While only Nordic and some West European continental countries are close to the double earner, supported carer ideal type, positive outliers prove that transformative gender relations in care can be construed not only in the richest and most generous welfare countries in Europe.

scholarly journals Fuzzy Decision Trees in Medical Decision Making Support Systems

2019 ◽  
Vol 8 (1) ◽  
pp. 37-42
Author(s):  
P Barach ◽  
V Levashenko ◽  
E Zaitseva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Trees ◽  
Clinical Medicine ◽  
Support Systems ◽  
Fuzzy Decision ◽  
Suitable Candidate ◽  
Decision Making Models ◽  
Predictive Data Mining ◽  
Fuzzy Decision Trees

Fuzzy decision trees represent classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information. Decision Making Support Systems are used widely in clinical medicine because decisions play an important role in diagnostic processes. Decision trees are a very suitable candidate for induction of simple decision-making models with the possibility of automatic learning. The goal of this paper is to demonstrate a new approach for predictive data mining models in clinical medicine. This approach is based on induction of fuzzy decision trees. This approach allows us to build decision-making modesl with different properties (ordered, stability etc.). Three new types of fuzzy decision trees (non-ordered, ordered and stable) are considered in the paper. Induction of these fuzzy decision trees is based on cumulative information estimates. Results of experimental investigation are presented. Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Using new approaches based on fuzzy decision trees allows to increase the prediction accuracy. Decision trees are a very suitable candidate for induction using simple decision-making models with the possibility of automatic and AI learning.

Fuzzy Decision Trees

2011 ◽  
pp. 696-703
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Decision Trees ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Entropy Measure ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Fuzzy Decision Trees

The inductive learning methodology known as decision trees, concerns the ability to classify objects based on their attributes values, using a tree like structure from which decision rules can be accrued. In this article, a description of decision trees is given, with the main emphasis on their operation in a fuzzy environment. A first reference to decision trees is made in Hunt et al. (1966), who proposed the Concept learning system to construct a decision tree that attempts to minimize the score of classifying chess endgames. The example problem concerning chess offers early evidence supporting the view that decision trees are closely associated with artificial intelligence (AI). It is over ten years later that Quinlan (1979) developed the early work on decision trees, to introduced the Interactive Dichotomizer 3 (ID3). The important feature with their development was the use of an entropy measure to aid the decision tree construction process (using again the chess game as the considered problem). It is ID3, and techniques like it, that defines the hierarchical structure commonly associated with decision trees, see for example the recent theoretical and application studies of Pal and Chakraborty (2001), Bhatt and Gopal (2005) and Armand et al. (2007). Moreover, starting from an identified root node, paths are constructed down to leaf nodes, where the attributes associated with the intermediate nodes are identified through the use of an entropy measure to preferentially gauge the classification certainty down that path. Each path down to a leaf node forms an ‘if .. then ..’ decision rule used to classify the objects. The introduction of fuzzy set theory in Zadeh (1965), offered a general methodology that allows notions of vagueness and imprecision to be considered. Moreover, Zadeh’s work allowed the possibility for previously defined techniques to be considered with a fuzzy environment. It was over ten years later that the area of decision trees benefited from this fuzzy environment opportunity (see Chang and Pavlidis, 1977). Since then there has been a steady stream of research studies that have developed or applied fuzzy decision trees (FDTs) (see recently for example Li et al., 2006 and Wang et al., 2007). The expectations that come with the utilisation of FDTs are succinctly stated by Li et al. (2006, p. 655); “Decision trees based on fuzzy set theory combines the advantages of good comprehensibility of decision trees and the ability of fuzzy representation to deal with inexact and uncertain information.” Chiang and Hsu (2002) highlight that decision trees has been successfully applied to problems in artificial intelligence, pattern recognition and statistics. They go onto outline a positive development the FDTs offer, namely that it is better placed to have an estimate of the degree that an object is associated with each class, often desirable in areas like medical diagnosis (see Quinlan (1987) for the alternative view with respect to crisp decision trees). The remains of this article look in more details at FDTs, including a tutorial example showing the rudiments of how an FDT can be constructed.

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