Fuzzy binary decision tree for biometric based personal authentication

Kumar, Amioy; Hanmandlu, M.; Gupta, H. M.

doi:10.1016/j.neucom.2012.06.016

Cited by 21 publications

(9 citation statements)

References 33 publications

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“…There are also some recently proposed fuzzy decision trees. [15][16][17][18] As for small-scale data sets or medium-scale data sets, the above-mentioned Concurrency Computat Pract Exper. 2019;31:e5268.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 13,14 extended fuzzy decision trees to the framework of axiomatic fuzzy set logic by improving membership functions used in the computing of fuzzy entropy.There are also some recently proposed fuzzy decision trees. [15][16][17][18] As for small-scale data sets or medium-scale data sets, the above-mentioned Concurrency Computat Pract Exper. 2019;31:e5268.…”

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A parallel tree node splitting criterion for fuzzy decision trees

Liu

Wang

et al. 2019

Concurrency and Computation

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Fuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small-scale or medium-scale data sets, and cannot directly deal with large-scale data sets on the account of some limiting factors such as memory capacity, execution time, and data complexity.Parallel computing is one way to overcome these problems; in particular, MapReduce is one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR-NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR-NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large-scale data sets. KEYWORDSfuzzy decision trees, fuzzy information gain, MapReduce, parallel computing INTRODUCTIONDecision trees are one of the most well-known researches to describe a decision-making process in light of existing knowledge. Each branch of a decision tree can be transformed into a decision rule, and all these decision rules can generate a decision rule base. The popularity of decision trees mainly arises from that the decision rules are more readily comprehensible than some other decision-making models such as neural networks. 1,2 Based on decision trees, there are many extensions 3 : Most of them are extensions or improvements of the well-known ID3 algorithm 4 and CART algorithm. 5 Fuzzy decision trees are one of the most popular extensions. They combine the symbolic decision trees with approximate reasoning offered by fuzzy representation. 6 The intent is to exploit the complementary advantages of the comprehensibility of decision trees and the uncertain information of fuzzy representation. 7,8Based on some splitting mechanisms, fuzzy decision trees recursively partition the training data into several subsets with some similar or same outputs in a top-down way. In particular, as one of the splitting mechanisms, fuzzy information theory makes a widespread influence in the growth of fuzzy decision trees. Weber 9 presented a well-known fuzzy Iterative Dichotomiser 3 (ID3) algorithm by modifying the information gain measure used to split a tree node for fuzzy representation. Umanol et al 10 proposed a new algorithm based on the probability of membership values to generate a fuzzy decision tree from numerical data, in which the fuzzy sets for each attribute are predefined by users. Ichihashi et al 11 realized fuzzy partitions by extracting fuzzy reasoning rules. An algebraic method to facilitate incremental learning is also employed. As knowledge inferences must be newly defined in fuz...

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A parallel tree node splitting criterion for fuzzy decision trees

Liu

Wang

et al. 2019

Concurrency and Computation

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“…support vector machine (SVM) [1], decision trees [2,3], K-nearest neighbor (KNN) [4,5], bayes model [6][7][8], neural network [9,10] etc, have been applied to text categorization. Although such methods have been extensively researched, yet the present automated text classifiers are still with fault and the effectiveness needs improvement.…”

Section: Introductionmentioning

confidence: 99%

Application of a staged learning-based resource allocation network to automatic text categorization

Song¹,

Peng²,

Park

2015

Neurocomputing

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“…Component-based face detection integrating AdaBoost learning and DT [21], hybrid of SVM-DT for face recognition [22], also can be referred. Kumar et al [23] proposed a new scheme using Fuzzy Binary Decision Tree (FBDT), which incorporated fuzzy gini index and fuzzy entropy for decision level fusion. Although there was an attempt using FDT and gini index in [24], its scope was limited.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution based score level fusion for multi-modal biometric systems

Mukherjee

Pal

Majumder

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores from different biometric sources into a single amalgamated matching score to be used by a classifier for further decision making. Differential Evolution (DE) has been employed to adjust these tunable parameters with the objective being the minimization of the overlapping area of the frequency distributions of genuine and imposter scores in the fused score space, which are estimated by Gaussian kernel density method to achieve higher level of accuracy. Experimental results show that, the proposed method outperforms the conventional score-level fusion rules (sum, product, tanh, exponential) when tested on two databases of 4 modalities (fingerprint, iris, left ear and right ear) of 200 and 516 users and thus confirms the effectiveness of score level fusion. The preliminary results provide adequate motivation towards future research in the line of the application of meta-heuristics in score level fusion.

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Fuzzy binary decision tree for biometric based personal authentication

Cited by 21 publications

References 33 publications

A parallel tree node splitting criterion for fuzzy decision trees

A parallel tree node splitting criterion for fuzzy decision trees

Application of a staged learning-based resource allocation network to automatic text categorization

Differential evolution based score level fusion for multi-modal biometric systems

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