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.

Fuzzy Decision-Tree-Based Analysis of Databases

2011 ◽  
pp. 760-783
Author(s):  
Malcolm Beynon
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Inductive Learning ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Fuzzy Environment ◽  
The United Kingdom ◽  
Fuzzy Representation ◽  
Fuzzy Decision Trees ◽  
Low Pay

The general fuzzy decision tree approach encapsulates the benefits of being an inductive learning technique to classify objects, utilising the richness of the data being considered, as well as the readability and interpretability that accompanies its operation in a fuzzy environment. This chapter offers a description of fuzzy decision tree based research, including the exposition of small and large fuzzy decision trees to demonstrate their construction and practicality. The two large fuzzy decision trees described are associated with a real application, namely, the identification of workplace establishments in the United Kingdom that pay a noticeable proportion of their employees less than the legislated minimum wage. Two separate fuzzy decision tree analyses are undertaken on a low-pay database, which utilise different numbers of membership functions to fuzzify the continuous attributes describing the investigated establishments. The findings demonstrate the sensitivity of results when there are changes in the compactness of the fuzzy representation of the associated data.

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.

Fuzzy Decision Trees

2011 ◽  
pp. 382-390
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Binary Tree ◽  
Fuzzy Decision ◽  
Data Set ◽  
Fuzzy Environment ◽  
Id3 Algorithm ◽  
Backtrack Algorithm ◽  
The 1960S ◽  
Fuzzy Decision Trees

The first (crisp) decision tree techniques were introduced in the 1960s (Hunt, Marin, & Stone, 1966), their appeal to decision makers is due in no part to their comprehensibility in classifying objects based on their attribute values (Janikow, 1998). With early techniques such as the ID3 algorithm (Quinlan, 1979), the general approach involves the repetitive partitioning of the objects in a data set through the augmentation of attributes down a tree structure from the root node, until each subset of objects is associated with the same decision class or no attribute is available for further decomposition, ending in a number of leaf nodes. This article considers the notion of decision trees in a fuzzy environment (Zadeh, 1965). The first fuzzy decision tree (FDT) reference is attributed to Chang and Pavlidis (1977), which defined a binary tree using a branch-bound-backtrack algorithm, but limited instruction on FDT construction. Later developments included fuzzy versions of crisp decision techniques, such as fuzzy ID3, and so forth (see Ichihashi, Shirai, Nagasaka, & Miyoshi, 1996; Pal & Chakraborty, 2001) and other versions (Olaru & Wehenkel, 2003).

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).

Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree

2014 ◽  
Vol 6 (4) ◽  
pp. 346 ◽  
Author(s):  
Swathi Jamjala Narayanan ◽  
Rajen B. Bhatt ◽  
Ilango Paramasivam ◽  
M. Khalid ◽  
B.K. Tripathy
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Neuro Fuzzy ◽  
Fuzzy Decision Trees

scholarly journals Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 279 ◽  
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Yuhui Wang ◽  
Jianliang He ◽  
Chenguang Yang
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Incomplete Information ◽  
Time Series Data ◽  
Short Term Memory ◽  
Prediction Method ◽  
Series Data ◽  
Air Combat ◽  
Solid Foundation ◽  
Long Short Term Memory

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

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.

Fuzzy Decision Rule Construction Using Fuzzy Decision Trees

2010 ◽  
pp. 104-124
Author(s):  
Malcolm J. Beynon ◽  
Paul Jones
Keyword(s):  
Decision Trees ◽  
Decision Rules ◽  
Activity Levels ◽  
Fuzzy Decision ◽  
Data Set ◽  
Online Activity ◽  
Fuzzy Environment ◽  
E Learning ◽  
Fuzzy Decision Trees ◽  
University Course

This chapter considers the soft computing approach called fuzzy decision trees (FDT), a form of classification analysis. The consideration of decision tree analysis in a fuzzy environment brings further interpretability and readability to the constructed ‘if .. then ..’ decision rules. Two sets of FDT analyses are presented, the first on a small example data set, offering a tutorial on the rudiments of one FDT technique. The second FDT analysis considers the investigation of an e-learning database, and the elucidation of the relationship between weekly online activity of students and their final mark on a university course module. Emphasis throughout the chapter is on the visualization of results, including the fuzzification of weekly online activity levels of students and overall performance.

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