On combining classifiers

Kittler, Josef; Hatef, M.; Duin, Robert P. W.; Matas, Jiřı́

doi:10.1109/34.667881

Cited by 4,501 publications

(2,004 citation statements)

References 37 publications

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“…This discriminant function, obtained by averaging the BF over time, corresponds to a majority vote rule for assigning each trial to the accept or reject condition. This rule is known to be more robust to errors and to outperform other classifiers, such as a product rule (Kittler et al, 1998;Tax et al, 2000). For every latency and trial of the training dataset we computed all possible combinations of the Bayes Factor (of k and k ) and selected the most discriminant pair of template maps.…”

Section: Eeg Analysis 251 Multivariate Eeg Decoding Based On Accummentioning

confidence: 99%

Quantifying the time for accurate EEG decoding of single value-based decisions

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

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h i g h l i g h t s• We outline a novel method for assessing time-unlocked topographic EEG activity.• We apply this method for accurately predicting accept vs. reject decisions.• The time when decisions have been made is modulated by task difficulty.• It occurs well before subjects' button press (∼340 ms).• This time is compatible with a diffusion process, evaluated at behavioral level. a r t i c l e i n f o b s t r a c tBackground: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-bytrial basis. Typical latencies of when decisions are made were detected at ∼500 ms for 'Easy' vs. ∼700 ms for 'Hard' decisions, well before subjects' response (∼340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with existing method(s):We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

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Section: Eeg Analysis 251 Multivariate Eeg Decoding Based On Accummentioning

confidence: 99%

Quantifying the time for accurate EEG decoding of single value-based decisions

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

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“…These include class probability combining strategies such as the Min Rule, the Max Rule, the Median Rule and the Majority Vote Rule [10]. For instance, the Majority Vote Rule chooses the segmentation label for each pixel based on what the majority of observers agree on; this simple method, however, does not take into consideration the variability in quality or performance among the voters and also does not incorporate the prior knowledge regarding segmentations.…”

Section: Background On Multiple Observer Segmentation Combination Metmentioning

confidence: 99%

Balancing the Role of Priors in Multi-Observer Segmentation Evaluation

Zhu

Huang

Wang

et al. 2008

J Sign Process Syst Sign Image Video Technol

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Comparison of a group of multiple observer segmentations is known to be a challenging problem. A good segmentation evaluation method would allow different segmentations not only to be compared, but to be combined to generate a "true" segmentation with higher consensus. Numerous multi-observer segmentation evaluation approaches have been proposed in the literature, and STAPLE in particular probabilistically estimates the true segmentation by optimal combination of observed segmentations and a prior model of the truth. An Expectation-Maximization (EM) algorithm, STAPLE'S convergence to the desired local minima depends on good initializations for the truth prior and the observer-performance prior. However, accurate modeling of the initial truth prior is nontrivial. Moreover, among the two priors, the truth prior always dominates so that in certain scenarios when meaningful observer-performance priors are available, STAPLE can not take advantage of that information. In this paper, we propose a Bayesian decision formulation of the problem that permits the two types of prior knowledge to be integrated in a complementary NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript manner in four cases with differing application purposes: (1) with known truth prior; (2) with observer prior; (3) with neither truth prior nor observer prior; and (4) with both truth prior and observer prior. The third and fourth cases are not discussed (or effectively ignored) by STAPLE, and in our research we propose a new method to combine multiple-observer segmentations based on the maximum a posterior (MAP) principle, which respects the observer prior regardless of the availability of the truth prior. Based on the four scenarios, we have developed a web-based software application that implements the flexible segmentation evaluation framework for digitized uterine cervix images. Experiment results show that our framework has flexibility in effectively integrating different priors for multi-observer segmentation evaluation and it also generates results comparing favorably to those by the STAPLE algorithm and the Majority Vote Rule.

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“…It has been often found that in a setting where confidences for all labels can be obtained and the most likely one should be chosen, four basic ways of combining confidences, the sum, product, min and max rules, can be very effective in spite of their simplicity [5]. Also in this work these decision schemes are used.…”

Section: Final Confidence and Decision Mechanismsmentioning

confidence: 99%

On Adaptive Confidences for Critic-Driven Classifier Combining

Aksela¹,

Laaksonen²

2005

Pattern Recognition and Data Mining

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Abstract. When combining classifiers in order to improve the classification accuracy, precise estimation of the reliability of each member classifier can be very beneficial. One approach for estimating how confident we can be in the member classifiers' results being correct is to use specialized critics to evaluate the classifiers' performances. We introduce an adaptive, critic-based confidence evaluation scheme, where each critic can not only learn from the behavior of its respective classifier, but also strives to be robust with respect to changes in its classifier. This is accomplished via creating distribution models constructed from the classifier's stored output decisions, and weighting them in a manner that attempts to bring robustness toward changes in the classifier's behavior. Experiments with handwritten character classification showing promising results are presented to support the proposed approach.

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On combining classifiers

Cited by 4,501 publications

References 37 publications

Quantifying the time for accurate EEG decoding of single value-based decisions

Quantifying the time for accurate EEG decoding of single value-based decisions

Balancing the Role of Priors in Multi-Observer Segmentation Evaluation

On Adaptive Confidences for Critic-Driven Classifier Combining

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