An interactive two-level architecture for a memory network pattern classifier

Fairhurst, Michael; Wahab, H. M. S. Abdel

doi:10.1016/0167-8655(90)90022-t

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…For the more difficult objects more complex procedures, possibly based on different features, are used (sequential or pipelined [17], [7], or hierarchical [24], [16]). Other studies in the gradual reduction of the set of possible classes are [8], [6], [14], [21]. The combination of ensembles of neural networks (based on different initialisations), has been studied in the neural network literature, e.g., [11], [4], [5], [10], [15], [18].…”

Section: Introductionmentioning

confidence: 99%

On combining classifiers

Kittler

Hatef

Duin

et al. 1998

IEEE Trans. Pattern Anal. Machine Intell.

4,528

1,892

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We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

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Section: Introductionmentioning

confidence: 99%

On combining classifiers

Kittler

Hatef

Duin

et al. 1998

IEEE Trans. Pattern Anal. Machine Intell.

4,528

1,892

View full text Add to dashboard Cite

show abstract

“…Expanding the product and neglecting any terms of second and higher order, the right-hand side of (10) can be re-written as follows:…”

Section: Sum Rulementioning

confidence: 99%

Improving Breast Cancer Risk Stratification Using Resonance-Frequency Electrical Impedance Spectroscopy Through Fusion of Multiple Classifiers

et al. 2010

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This study aims to improve breast cancer risk stratification. A seven-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system was designed, assembled, and utilized to establish a data set of examinations from 174 women. Three classifiers, including artificial neural network (ANN), support vector machine (SVM), and Gaussian mixture model (GMM), were independently developed to predict the likelihood of each woman to be recommended for biopsy. The performances of these classifiers were compared, and seven fusion methods for integrating these classifiers were investigated. The results showed that among the three classifiers, the ANN yielded the highest performance with an area under the curve (AUC) of 0.81 for the receiver operating characteristic (ROC), while SVM and GMM achieved AUCs of 0.80 and 0.78, respectively. Improvements of up to 3% were obtained using fusion of the three classifiers, with the largest improvement obtained using either a "minimum score" rule or a "weighted sum" rule. Comparing different combinations of two out of the three classifiers, the weighted sum rule provided the most robust and consistent results, with AUCs of 0.81, 0.83, and 0.82 for the different combinations of ANN and SVM, ANN and GMM, and SVM and GMM, respectively. Furthermore, at 90% specificity, the ANN, the weighted sum- and min rule-based classifiers, all detected 67% of the verified cancer cases as compared with 50, 50, and 60% detection of the high risk cases, respectively. The study demonstrated that REIS examinations provide relevant information for developing breast cancer risk stratification tools and that using fusion of several not-fully-correlated classifiers can improve classification performance.

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“…They can be either sequential or pipelined [1], [7], or hierarchical [8], [9]. Other studies done in the gradual reduction of the set of possible classes are shown in [3], [4], [6]. The combination of ensembles of neural networks (based on different initializations) has been studied in the neural network literature [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Designing Efficient Multiple Classifier

Singh

Samal

Lahiri

2005

Advances in Biometrics

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Abstract. In this paper we have shown that systematic incorporation of decision from various classifiers following a simple decision decomposition rule, gives better decision in comparison to the existing multiple classifier systems. In our method each classifier were graded according to their effectiveness of providing more accurate results. This approach first utilizes the best classifier. If this classifier classifies the test sample into more than one class or fails to classify the test data then the feature next to the best is summoned to finish up the remaining part of the classification. The continuation of this process, along with the judicious selection of classifiers, yields better efficiency in identifying a single class for the test data. The results obtained after the experiments on a set of fingerprint images shows the effectiveness of our proposed classifier.

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An interactive two-level architecture for a memory network pattern classifier

Cited by 13 publications

References 7 publications

On combining classifiers

On combining classifiers

Improving Breast Cancer Risk Stratification Using Resonance-Frequency Electrical Impedance Spectroscopy Through Fusion of Multiple Classifiers

A Novel Strategy for Designing Efficient Multiple Classifier

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