A simple method for combining estimates to improve the overall error rates in classification

Mojirsheibani, Majid

doi:10.1007/s00180-015-0571-0

Cited by 7 publications

(11 citation statements)

References 16 publications

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“…We will prove that these properties hold for the possibilistic approach that we introduce in the next section at least asymptotically for some of them. Observe that (3) asymptotically implies properties (a) to (c) so the added value of our approach as compared [1] relies on memory complexity and scalability w.r.t. as numerical experiments will illustrate in section 4.…”

Section: Desirable Properties For Classifier Combinationmentioning

confidence: 99%

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SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

Albardan

Klein²,

Colot³

2020

Expert Systems with Applications

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We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset.We propose a new approach to aggregate the learner predictions in the possibility theory framework. For each classifier prediction, we build a possibility distribution assessing how likely the classifier prediction is correct using frequentist probabilities estimated on the validation set. The possibility distributions are aggregated using an adaptive t-norm that can accommodate dependency and poor accuracy of the classifier predictions. We prove that the proposed approach possesses a number of desirable classifier combination robustness properties.

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Section: Desirable Properties For Classifier Combinationmentioning

confidence: 99%

“…Although properties (a) to (c) are not as strong as (3), adaSPOCC is a scalable aggregation technique as the number of parameters it requires to learn from the validation set is in O ( K) while the number of parameters to learn from D val in [1] is in O K and is therefore doomed to overfit when K is large.…”

Section: Fully Adaptive Aggregationmentioning

confidence: 99%

SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

Albardan

Klein²,

Colot³

2020

Expert Systems with Applications

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“…In the second step, for each divergence, a very simple predictive model is fit per cluster. The final step provides an adaptive global predictive model by aggregating, thanks to a consensus idea introduced by Mojirsheibani (1999), several models built for the different instances, corresponding to the different Bregman divergences (see also Mojirsheibani (2000); Balakrishnan and Mojirsheibani (2015); Biau et al (2016); Fischer and Mougeot (2019)). We name this procedure the KFC procedure for K-means/Fit/Consensus.…”

Section: Introductionmentioning

confidence: 99%

KFC: A clusterwise supervised learning procedure based on the aggregation of distances

Has

Fischer

Mougeot

2021

Journal of Statistical Computation and Simulation

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Nowadays, many machine learning procedures are available on the shelve and may be used easily to calibrate predictive models on supervised data. However, when the input data consists of more than one unknown cluster, and when different underlying predictive models exist, fitting a model is a more challenging task. We propose, in this paper, a procedure in three steps to automatically solve this problem. The KFC procedure aggregates different models adaptively on data. The first step of the procedure aims at catching the clustering structure of the input data, which may be characterized by several statistical distributions. It provides several partitions, given the assumptions on the distributions. For each partition, the second step fits a specific predictive model based on the data in each cluster. The overall model is computed by a consensual aggregation of the models corresponding to the different partitions. A comparison of the performances on different simulated and real data assesses the excellent performance of our method in a large variety of prediction problems.

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“…Note that more regular versions, based on smoothing kernels, have also been developed (Mojirsheibani (2000)). A numerical comparison study of several combining schemes is available in Mojirsheibani (2002b), and recently, a variant of the method has been proposed in Balakrishnan and Mojirsheibani (2015). This strategy has just been adapted in the regression framework by Biau et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Aggregation using input–output trade-off

Fischer

Mougeot

2019

Journal of Statistical Planning and Inference

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In this paper, we introduce a new learning strategy based on a seminal idea of Mojirsheibani (1999, 2000, 2002a, 2002b), who proposed a smart method for combining several classifiers, relying on a consensus notion. In many aggregation methods, the prediction for a new observation x is computed by building a linear or convex combination over a collection of basic estimators r1(x), . . . , rm(x) previously calibrated using a training data set. Mojirsheibani proposes to compute the prediction associated to a new observation by combining selected outputs of the training examples. The output of a training example is selected if some kind of consensus is observed: the predictions computed for the training example with the different machines have to be "similar" to the prediction for the new observation. This approach has been recently extended to the context of regression in Biau et al. (2016).In the original scheme, the agreement condition is actually required to hold for all individual estimators, which appears inadequate if there is one bad initial estimator. In practice, a few disagreements are allowed ; for establishing the theoretical results, the proportion of estimators satisfying the condition is required to tend to 1.In this paper, we propose an alternative procedure, mixing the previous consensus ideas on the predictions with the Euclidean distance computed between entries. This may be seen as an alternative approach allowing to reduce the effect of a possibly bad estimator in the initial list, using a constraint on the inputs.We prove the consistency of our strategy in classification and in regression. We also provide some numerical experiments on simulated and real data to illustrate the benefits of this new aggregation method. On the whole, our practical study shows that our method may perform much better than the original combination technique, and, in particular, exhibit far less variance. We also show on simulated examples that this procedure mixing inputs and outputs is still robust to high dimensional inputs.

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A simple method for combining estimates to improve the overall error rates in classification

Cited by 7 publications

References 16 publications

SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

KFC: A clusterwise supervised learning procedure based on the aggregation of distances

Aggregation using input–output trade-off

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