Routing Memento Requests Using 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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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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Text dependent speaker verification using binary classifiers

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) ◽

10.1109/icassp.1998.674384 ◽

2002 ◽

Author(s):

D. Genoud ◽

M. Moreira ◽

E. Mayoraz

Keyword(s):

Speaker Verification ◽

Binary Classifiers ◽

Text Dependent Speaker Verification

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Hand Detection Using Cascade of Softmax Classifiers

Advances in Multimedia ◽

10.1155/2018/9204854 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Yan-Guo Zhao ◽

Feng Zheng ◽

Zhan Song

Keyword(s):

Logistic Regression ◽

Real Time ◽

Sliding Window ◽

Detection Accuracy ◽

Hand Posture ◽

Hand Detection ◽

Cascade Structure ◽

Real Time Applications ◽

Binary Classifiers ◽

Background Pattern

Sliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, an efficient cascade detector that integrates multiple softmax-based binary (SftB) models and a softmax-based multiclass (SftM) model is investigated to perform multiclass posture detection in parallel. The SftB models are used to distinguish the predefined postures from the background regions, and the SftM model is applied to discriminate among all the predefined hand posture categories. Another usage of the cascade structure is that it could effectively decompose the complexity of background pattern space and therefore improve the detection accuracy. In addition, to balance the detection accuracy and efficiency, the HOG features of increasing resolutions will be adopted by classifiers of increasing stage-levels in the cascade structure. The experiments are implemented under various scenarios with complicated background and challenging lightings. Results show the superiority of the proposed SftB classifiers over the traditional binary classifiers such as logistic regression, as well as the accuracy and efficiency improvements brought by the softmax-based cascade architecture compared with the noncascade multiclass softmax detectors.

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Merging Quality Estimation for Binary Decision Diagrams with Binary Classifiers

Machine Learning, Optimization, and Data Science - Lecture Notes in Computer Science ◽

10.1007/978-3-030-37599-7_37 ◽

2019 ◽

pp. 445-457

Author(s):

Nikolaus Frohner ◽

Günther R. Raidl

Keyword(s):

Binary Decision Diagrams ◽

Decision Diagrams ◽

Quality Estimation ◽

Binary Decision ◽

Binary Classifiers

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The Computational Complexity of Understanding Binary Classifier Decisions

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12359 ◽

2021 ◽

Vol 70 ◽

Author(s):

Stephan Waeldchen ◽

Jan Macdonald ◽

Sascha Hauch ◽

Gitta Kutyniok

Keyword(s):

Polynomial Time Algorithm ◽

Time Algorithm ◽

Boolean Circuits ◽

Binary Classifier ◽

Minimal Set ◽

Limited Size ◽

Approximation Factor ◽

Special Cases ◽

Relevant Variables ◽

Binary Classifiers

For a d-ary Boolean function Φ: {0, 1}d → {0, 1} and an assignment to its variables x = (x1, x2, . . . , xd) we consider the problem of finding those subsets of the variables that are sufficient to determine the function value with a given probability δ. This is motivated by the task of interpreting predictions of binary classifiers described as Boolean circuits, which can be seen as special cases of neural networks. We show that the problem of deciding whether such subsets of relevant variables of limited size k ≤ d exist is complete for the complexity class NPPP and thus, generally, unfeasible to solve. We then introduce a variant, in which it suffices to check whether a subset determines the function value with probability at least δ or at most δ − γ for 0 < γ < δ. This promise of a probability gap reduces the complexity to the class NPBPP. Finally, we show that finding the minimal set of relevant variables cannot be reasonably approximated, i.e. with an approximation factor d1−α for α > 0, by a polynomial time algorithm unless P = NP. This holds even with the promise of a probability gap.

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Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica

10.21203/rs.3.rs-1030905/v1 ◽

2021 ◽

Author(s):

Erin Bryce ◽

Luigi Lombardo ◽

Cees van Westen ◽

Hakan Tanyas ◽

Daniela Castro-Camilo

Keyword(s):

Spatial Information ◽

Landslide Hazard ◽

Sufficient Information ◽

Tropical Regions ◽

Landslide Occurrence ◽

Hurdle Models ◽

Modelling Framework ◽

Markovian Representation ◽

Binary Classifiers ◽

A Chain

Abstract Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matérn covariance function based on the stochastic partial differential equation (SPDE) approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where Hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind.

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