A new design of multiple classifier system and its application to the classification of time series data

Chen, Lei; Kamel, Mohamed S.

doi:10.1109/icsmc.2007.4413686

Cited by 5 publications

(3 citation statements)

References 71 publications

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“…Seppo Puuronen and team [14] propose a similarity evaluation technique that uses a training set consisting predicates that define relationships within the three sets: the set of instances, the set of classes, and the set of classifiers. Lei Chen and Mohamed S. Kamel [15] propose the scheme of Multiple Input Representation-Adaptive Ensemble Generation and Aggregation (MIR-AEGA) for the classification of time series data. Kai Jiang et.al.…”

Section: Review Of the Related Literaturementioning

confidence: 99%

Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

Lakshmi¹

2013

IJMECS

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The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability

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Section: Review Of the Related Literaturementioning

confidence: 99%

Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

Lakshmi¹

2013

IJMECS

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“…Diversity in the learning stage is achieved using MBoost [80], a variant of AdaBoost designed to simultaneously boost multiple weak learners. The work of Chen and Kamel [15] also introduces diversity at both stages. Diversity at the input is achieved considering several preprocessing alternatives.…”

Section: Time Series Classificationmentioning

confidence: 99%

Stacking for multivariate time series classification

Prieto

Alonso-González

Rodrı́guez

2013

Pattern Anal Applic

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This work presents a novel approach to multivariate time series classification. The method exploits the multivariate structure of the time series and the possibilities of the stacking ensemble method. The basics of the method may be described in three steps: first, decomposing the multivariate time series on its constituent univariate time series; second, inducing a classifier for each univariate time series plus and additional multivariate classifier for the whole time series; third, creating the final multivariate time series classifier stacking the previous classifiers. The ensemble obtained has the potential to improve the accuracy of the single multivariate time series classifier. Several configurations of the stacking method have been tested on seven multivariate time series data sets. In five out of seven data sets, the proposed method obtains the smallest error rate. Moreover, in two out of seven data sets, stacking only the univariate time series classifiers provides the best results. The experimental results show that when a multivariate time series method does not produce an accurate classifier, stacking it with univariate time series classifiers is an alternative worthy of consideration.

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“…Seppo Puuronen et al, [20] propose a similarity evaluation technique that uses a training set consisting predicates that define relationships within the three sets: the set of instances, the set of classes, and the set of classifiers. Lei Chen and Mohamed S. Kamel [18] propose the scheme of Multiple Input RepresentationAdaptive Ensemble Generation and Aggregation(MIR-AEGA) for the classification of time series data. Kai Jiang et.al.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Statistical Significance of Classifiers used in the Prediction of Tuberculosis

Asha¹

2012

IOSRJCE

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A new design of multiple classifier system and its application to the classification of time series data

Cited by 5 publications

References 71 publications

Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

Stacking for multivariate time series classification

Estimating the Statistical Significance of Classifiers used in the Prediction of Tuberculosis

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