Experimental evaluation of expert fusion strategies

Alkoot, Fuad M.; Kittler, Josef

doi:10.1016/s0167-8655(99)00107-5

Cited by 91 publications

(54 citation statements)

References 17 publications

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“…These rules have the advantage of being simple and not requiring prior training. In this and subsequent studies [38,39], it was found that the product rule is superior to the sum rule when estimation errors are low, but the performance of the product rule deteriorates dramatically as estimation errors increase. We have used both sum and product rules here.…”

Section: Fusion Methodsmentioning

confidence: 69%

Information fusion approaches to the automatic pronunciation of print by analogy

Damper

Marchand

2006

Information Fusion

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Automatic pronunciation of words from their spelling alone is a hard computational problem, especially for languages like English and French where there is only a partially consistent mapping from letters to sound. Currently, the best known approach uses an inferential process of analogy with other words listed in a dictionary of spellings and corresponding pronunciations. However, the process produces multiple candidate pronunciations and little or no theory exists to guide the choice among them. Rather than committing to one specific heuristic scoring method, it may be preferable to use multiple strategies (i.e., soft experts) and then employ information fusion techniques to combine them to give a final result. In this paper, we compare four different fusion schemes, using three different dictionaries (with different codings for specifying the pronunciations) as the knowledge base for analogical reasoning. The four schemes are: fusion of raw scores; rank fusion using Borda counting; rank fusion using non-uniform values; and rank fusion using non-uniform values weighted by a measure of prior performance of the experts. All possible combinations of five different expert strategies are studied. Although all four fusion schemes outperformed the single best strategy, results show clear superiority of rank fusion over the other methods.

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Section: Fusion Methodsmentioning

confidence: 69%

Information fusion approaches to the automatic pronunciation of print by analogy

Damper

Marchand

2006

Information Fusion

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“…based on fingerprint and iris recognition, and the final decision was taken by using an AND operator. Common theoretical framework for combining classifiers using sum rule, median rule, max and min rule were analyzed by Alkoot and Kittler 6 under the most restrictive assumptions and observed that sum rule outperforms other classifier combination schemes. Ross and Jain 7 presented experimental results by combining three biometric modalities (face, fingerprint, and hand geometry) and stated that the sum rule outperformed better than the decision tree and linear discriminant classifiers.…”

Section: Related Workmentioning

confidence: 99%

Curvelet and ridgelet based multimodal biometric recognition system using weighted similarity approach

Arivazhagan¹,

Sekar²,

Priyadharshini³

2014

DSJ

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“…We use the mean of these N probabilities to give the ensemble prediction. This fusion strategy (equivalent to the sum of experts) was chosen because it is a simple and effective fusion strategy (see Alkoot and Kittler (1999)) that outputs a probability rather than a binary value, and because its predictions were found to be similar to the majority vote (the fusion strategy typically used in ensemble methods).…”

Section: Probabilistic Predictions For Route Blockagementioning

confidence: 99%

Identification of Robust Terminal-Area Routes in Convective Weather

Pfeil

Balakrishnan

2012

Transportation Science

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Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic Flow Management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the Vertically Integrated Liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal-area route will be blocked.Given a flight route through the terminal-area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminalarea operations, by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, while the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materializes.

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Experimental evaluation of expert fusion strategies

Cited by 91 publications

References 17 publications

Information fusion approaches to the automatic pronunciation of print by analogy

Information fusion approaches to the automatic pronunciation of print by analogy

Curvelet and ridgelet based multimodal biometric recognition system using weighted similarity approach

Identification of Robust Terminal-Area Routes in Convective Weather

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