Journal ArticleDOI
Improving learning accuracy of fuzzy decision trees by hybrid neural networks
TLDR
The weighted FDT, in which the reasoning mechanism incorporates the trained LW and GW, significantly improves the FDTs' learning accuracy while keeping the F DT comprehensibility.Abstract:
Although the induction of fuzzy decision tree (FDT) has been a very popular learning methodology due to its advantage of comprehensibility, it is often criticized to result in poor learning accuracy. Thus, one fundamental problem is how to improve the learning accuracy while the comprehensibility is kept. This paper focuses on this problem and proposes using a hybrid neural network (HNN) to refine the FDT. This HNN, designed according to the generated FDT and trained by an algorithm derived in this paper, results in a FDT with parameters, called weighted FDT. The weighted FDT is equivalent to a set of fuzzy production rules with local weights (LW) and global weights (GW) introduced in our previous work (1998). Moreover, the weighted FDT, in which the reasoning mechanism incorporates the trained LW and GW, significantly improves the FDTs' learning accuracy while keeping the FDT comprehensibility. The improvements are verified on several selected databases. Furthermore, a brief comparison of our method with two benchmark learning algorithms, namely, fuzzy ID3 and traditional backpropagation, is made. The synergy between FDT induction and HNN training offers new insight into the construction of hybrid intelligent systems with higher learning accuracy.read more
Citations
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Journal ArticleDOI
A complete fuzzy decision tree technique
Cristina Olaru,Louis Wehenkel +1 more
TL;DR: This method combines tree growing and pruning, to determine the structure of the soft decision tree, with refitting and backfitting, to improve its generalization capabilities.
Journal ArticleDOI
Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy
Xizhao Wang,Chun-Ru Dong +1 more
TL;DR: A new rule-refinement scheme is proposed that is based on the maximization of fuzzy entropy on the training set, which is expected to have the advantages of improving the generalization capability of initial fuzzy IF-THEN rules and simultaneously overcoming the overfitting of refinement.
Journal ArticleDOI
A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations
TL;DR: A fuzzy qualitative framework based on clustering techniques is presented to connect numerical and symbolic robot representations to provide a unified representation by combining symbolic or qualitative functions and numerical sensing and control tasks in the context of intelligent robotics.
Journal ArticleDOI
Fuzzy Qualitative Robot Kinematics
TL;DR: A fuzzy qualitative version of robot kinematics is proposed with the goal of bridging the gap between symbolic or qualitative functions and numerical sensing and control tasks for intelligent robotics and an aggregation operator to extract robot behaviors is proposed.
Journal ArticleDOI
Fuzzy Qualitative Human Motion Analysis
Chee Seng Chan,Honghai Liu +1 more
TL;DR: A fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition is proposed by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms and consistently outperforms conventional hidden Markov model and fuzzy HMM.
References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
Induction of fuzzy decision trees
Yufei Yuan,Michael J. Shaw +1 more
TL;DR: A fuzzy decision tree induction method, which is based on the reduction of classification ambiguity with fuzzy evidence, is developed, which represents classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information.
Journal ArticleDOI
Fuzzy min-max neural networks. I. Classification
TL;DR: The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier.
Journal ArticleDOI
Fuzzy decision trees: issues and methods
TL;DR: This work presents another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation, to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation.