Geometric programming for aggregation of binary classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

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Faculty Opinions recommendation of The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725375858.793530299 ◽

2017 ◽

Author(s):

Michael Barnes ◽

David Watson

Keyword(s):

Imbalanced Datasets ◽

Binary Classifiers

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Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

Current Drug Discovery Technologies ◽

10.2174/1570163816666190411110331 ◽

2020 ◽

Vol 17 (3) ◽

pp. 365-375

Author(s):

Vasyl Kovalishyn ◽

Diana Hodyna ◽

Vitaliy O. Sinenko ◽

Volodymyr Blagodatny ◽

Ivan Semenyuta ◽

...

Keyword(s):

Machine Learning ◽

Isonicotinic Acid ◽

Acid Hydrazide ◽

Predictive Ability ◽

Antitubercular Activity ◽

Biological Evaluation ◽

Starting Point ◽

Binary Classifiers ◽

Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

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Routing Memento Requests Using Binary Classifiers

Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries - JCDL '16 ◽

10.1145/2910896.2910899 ◽

2016 ◽

Cited By ~ 5

Author(s):

Nicolas J. Bornand ◽

Lyudmila Balakireva ◽

Herbert Van de Sompel

Keyword(s):

Binary Classifiers

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Interclass Interference Suppression in Multi-Class Problems

Applied Sciences ◽

10.3390/app11010450 ◽

2021 ◽

Vol 11 (1) ◽

pp. 450

Author(s):

Jinfu Liu ◽

Mingliang Bai ◽

Na Jiang ◽

Ran Cheng ◽

Xianling Li ◽

...

Keyword(s):

Classification Accuracy ◽

Cross Validation ◽

Selection Process ◽

Interference Suppression ◽

Generalization Ability ◽

Suppression Effect ◽

Binary Classifiers ◽

The One ◽

Fold Cross Validation ◽

Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

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New Method to Posynomial Geometric Programming of Trapezoidal Fuzzy Numbers

Fuzzy Information and Engineering ◽

10.1007/s12543-013-0147-z ◽

2013 ◽

Vol 5 (3) ◽

pp. 373-380

Author(s):

Zeinab Kheiri ◽

Faezeh Zahmatkesh ◽

Bing-Yuan Cao

Keyword(s):

Geometric Programming ◽

Fuzzy Numbers ◽

New Method ◽

Trapezoidal Fuzzy Numbers

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OTEC Power System Modeling, Analysis and Design via Geometric Programming

Journal of Energy Resources Technology ◽

10.1115/1.3227866 ◽

1980 ◽

Vol 102 (3) ◽

pp. 154-159 ◽

Cited By ~ 1

Author(s):

A. Lavi

Keyword(s):

Nonlinear Programming ◽

Power System ◽

Geometric Programming ◽

Power Plants ◽

System Analysis ◽

System Modeling ◽

Analytical Treatment ◽

Transfer Energy ◽

Analysis And Design ◽

Operating Variables

A complex power system may be modeled by a system of inequalities representing the constraints imposed by the physical laws: heat transfer, energy balance, cycle efficiency and so forth. The nature of the resulting mathematical model is such that the terms contain complex expressions involving the design and operating variables of the process. With the addition of an objective function involving the cost of major system components, a multivariable nonlinear programming problem can be formulated. Seldom does the model lend itself to analytical treatment. This paper is concerned with a specific formulation and solution of nonlinear programming problems which arise in the design of ocean thermal energy conversion (OTEC) power plants. The technique used is geometric programming, GP. It is shown that GP serves as an excellent tool for system analysis because it provides sensitivity information essential to the designer.

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