Decision Level Fusion of Intramodal Personal Identity Verification Experts

Kittler, Josef; Ballette, Marco; Czyz, J.; Roli, Fabio; Vandendorpe, Luc

doi:10.1007/3-540-45428-4_31

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…However, we could have included in this group other successful trainable combiners from the literature. One such candidate would be the so called behavior knowledge space method [22] which was found to be better than DTs in an identity verification study [23]. BKS is a multinomial classifier on the class label outputs which requires a look-up table of size which makes it inappropriate for even moderate size ensembles.…”

Section: Fewer Classifiers (Early Stopping Of Adaboost)mentioning

confidence: 99%

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"Fuzzy" versus "nonfuzzy" in combining classifiers designed by boosting

Kuncheva

2003

IEEE Trans. Fuzzy Syst.

132

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Abstract-Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers designed by Boosting. We ran two-fold cross-validation experiments on six benchmark data sets to compare the fuzzy and nonfuzzy combination methods. On the "fuzzy side" we used the fuzzy integral and the decision templates with different similarity measures. On the "nonfuzzy side" we tried the weighted majority vote as well as simple combiners such as the majority vote, minimum, maximum, average, product, and the Naive-Bayes combination. In our experiments, the fuzzy combination methods performed consistently better than the nonfuzzy methods. The weighted majority vote showed a stable performance, though slightly inferior to the performance of the fuzzy combiners.

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Section: Fewer Classifiers (Early Stopping Of Adaboost)mentioning

confidence: 99%

“…The Boosting algorithm for generating an ensemble is also explained there. The "fuzzy competitors" are presented in Section III: Fuzzy Integral [4], [8], [9], [18], [42], [43] and decision templates (DTs) [23], [29]. Section IV contains the experimental set up and the results.…”

mentioning

confidence: 99%

"Fuzzy" versus "nonfuzzy" in combining classifiers designed by boosting

Kuncheva

2003

IEEE Trans. Fuzzy Syst.

132

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“…We averaged four trained LSTM models with the lowest training and validation losses to improve model performance . We used this ensemble of trained models to reconstruct the 4000-waterbody area time series.…”

Section: Methodsmentioning

confidence: 99%

“…We averaged four trained LSTM models with the lowest training and validation losses to improve model performance. 52 We used this ensemble of trained models to reconstruct the 4000-waterbody area time series. As described earlier, due to the autoencoder formulation, the reconstructed time series have no gaps or outliers.…”

Section: Methodsmentioning

confidence: 99%

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Wander

Farruggia

Fuente

et al. 2024

Environ. Sci. Technol.

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Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

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“…Moreover, its performance may depend on the distribution of the classifiers' output which can affect the similarity measure in the intermediate feature space. DT fusion has been applied to various areas such as time series classification [76], biometrics [77], and intrusion detection [78], and compared with many other fusion techniques. The results are in general inconclusive, which confirms that there is no fusion method that outperforms all others in all applications.…”

mentioning

confidence: 99%

Feature-Based Methods for Landmine Detection with Ground Penetrating Radar

Gader¹,

Wilson²,

Bolton³

2012

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. The subject research was performed at the University of Florida between December 2005 and December 2008. The research was performed to support the ability to detect landmines in an automated fashion using ground-penetrating radar (GPR) array sensors employed in systems being studied by NVESD. The work was concerned with discovering and evaluating i) different types of features that, when extracted from signals associated with GPR signals captured over regions of earth, can help one identify the presence or absence of landmines and ABSTRACTThe subject research was performed at the University of Florida between December 2005 and December 2008. The research was performed to support the ability to detect landmines in an automated fashion using ground-penetrating radar (GPR) array sensors employed in systems being studied by NVESD. The work was concerned with discovering and evaluating i) different types of features that, when extracted from signals associated with GPR signals captured over regions of earth, can help one identify the presence or absence of landmines and landmine-like objects; ii) algorithms and techniques that can employ these features to distinguish between landmines and non-mines; and iii) fuse the results of multiple discriminators to yield improved discrimination performance. This document briefly reviews results of this research in each of these areas. Referenced papers are attached as appendices. Names of Under Graduate students supported PERCENT_SUPPORTED NAME FTE Equivalent:Total Number:The number of undergraduates funded by this agreement who graduated during this period with a degree in science, mathematics, engineering, or technology fields:The number of undergraduates funded by your agreement who graduated during this period and will continue to pursue a graduate or Ph.D. degree in science, mathematics, engineering, or technology fields:Number of graduating undergraduates who achieved a 3.5 GPA to 4.0 (4.0 max scale): Number of graduating undergraduates funded by a DoD funded Center of Excellence grant for Education, Research and Engineering:The number of undergraduates funded by your agreement who graduated during this period and intend to work for the Department of DefenseThe number of undergraduates funded by your agreement who graduated during this period and will receive scholarships or fellowships for further studies in science, mathematics, engineering or technology fields: Student Metrics This section only applies to graduating undergraduates supported by this agreement in this reporting periodThe number of undergraduates funded by this agreement who graduated during this period: 0.00 ...... Names of Personnel receiving masters degrees IntroductionThe subject research was performed at the U...

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Decision Level Fusion of Intramodal Personal Identity Verification Experts

Cited by 8 publications

References 7 publications

"Fuzzy" versus "nonfuzzy" in combining classifiers designed by boosting

"Fuzzy" versus "nonfuzzy" in combining classifiers designed by boosting

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Feature-Based Methods for Landmine Detection with Ground Penetrating Radar

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