Optimizing Majority Voting Based Systems Under a Resource Constraint for Multiclass Problems

Tiba, Attila; Hajdú, András; Terdik, György; Tomán, Henrietta

doi:10.1007/978-3-030-27550-1_67

Cited by 1 publication

(2 citation statements)

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“…To adopt our approach by following the logic of Algorithm 2, we need to determine a STOP value for the search based on p and p (calculated by ( 12)), and ̂ (calculated by ( 14)). However, since now the energy function is transformed by the terms p ,k in ( 22), we must borrow the corresponding theoretical results from Tiba et al (2019) to derive the mean q ̂ instead of (7) proposed in Algorithm 2. Accordingly, we had to find a continuous function F that fit to the values p ,k , which was evaluated by regression and resulted in F(x) = b∕(b + x a ∕(1 − x) a ) with a = −3.43 and ( 22)…”

Section: Optic Disc Detectionmentioning

confidence: 99%

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A stochastic approach to handle resource constraints as knapsack problems in ensemble pruning

et al. 2021

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Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function, which takes the common solution tools—such as dynamic programming—out of action. We introduce a novel stochastic approach that considers the energy as the joint probability function of the member accuracies. This type of knowledge can be efficiently incorporated in a stochastic search process as a stopping rule, since we have the information on the expected accuracy or, alternatively, the probability of finding more accurate ensembles. Experimental analyses of the created ensembles of pattern classifiers and object detectors confirm the efficiency of our approach over other pruning ones. Moreover, we propose a novel stochastic search method that better fits the energy, which can be incorporated in other stochastic strategies as well.

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Section: Optic Disc Detectionmentioning

confidence: 99%

“…Comparing SA with the proposed search strategy SHErLoCk on the OD detection problem = 101.7 , as also plotted in Fig.4. Now, by using Theorem 1 fromTiba et al (2019), we have gained q ̂…”

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confidence: 99%