binary classification
An Efficient Smooth Quantile Boost Algorithm for Binary Classification
... and binary classification in the frame- work of functional gradient ...In binary classification scenario, Zheng [7] considered the following model ...
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From ordinal ranking to binary classification
... cost-sensitive classification algorithm (CSOVO) by calling an under- lying binary classification algorithm, but also guarantees that a good cost-sensitive classifier can be obtained by combining a ...
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Leveraging Sub class Partition Information in Binary Classification and Its Application
... improve binary classification via multi-class categorization for applications where sub-class partition information within positive and/or negative classes is ...
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A novel logistic-NARX model as a classifier for dynamic binary classification
... for binary classification problems given that they provide probabilities of belonging or not to a particular ...popular classification techniques like random forests [30], support vector machines ...
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Deep graph regularized learning for binary classification
... state-of-the-art binary classification methods, including support vector machine (SVM) [18], GLR-based approaches [13], classic CNN-based classifier, and more recent, deep metric based k near- est neighbor ...
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Robustifying Binary Classification to Adversarial Perturbation
... Abstract— Despite the enormous success of machine learning models in various applications, most of these models lack resilience to (even small) perturbations in their input data. Hence, new methods to robustify machine ...
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Rule Discovery for Binary Classification Problem using ACO based Antminer
... ways. Classification is one of them. Classification is a data mining technique that assigns items to a predefined categories or classes or ...of classification is to predict the target class for the ...
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Binary classification algorithms
... The Logistic Regression model is applicable for binary classification or prediction when we have only two possible outcomes – positive/negative, yes/no, pass/fail, class_1/class_2, etc. There are cases with ...
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Consistent Binary Classification with Generalized Performance Metrics
... • State-of-the-art understanding of optimal decision making and consistent algorithms for binary classification is limited.. • It is well-known that accuracy (0-1 loss) is maximized (min[r] ...
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From convex to nonconvex: A loss function analysis for binary classification
... data classification can be studied in the framework of regularization theory as ill-posed ...two binary classification algorithms for binary classification, one for convex loss ...
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Evaluating Word Similarity Measure of Embeddings Through Binary Classification
... Figure 1. The approach design and workflow In this section, we describe the data sources and texts we used for training the embedding models. We start- ed with two well-known corpora. The first one is text8, a standard ...
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Using Hybrid and Diversity Based Adaptive Ensemble Method for Binary Classification
... DOI: 10.4236/ijis.2018.83003 44 International Journal of Intelligence Science better performance compared to that of using a single model [1]. A general workflow for solving classification problems using ensemble ...
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A Simple Regularized Multiple Criteria Linear Programs for Binary Classification
... On one hand, with the introduction of multiple objectives, MCMP has more powerful modeling capabilities than the single-objective program; On the other hand, MCMPs are always di ffi cult to be optimized than the opti- ...
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Various Performance Measures In Binary Classification –An Overview Of Roc Study
... of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out the analysis of their Receiver Operating Characteristic (ROC) ...
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Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine
... In order to evaluate the proposed classification algorithms, data set I of BCI competition III is adopted in this study. It is provided by University of Tübingen, Germany, Dept. of Computer Engineering (Prof. ...
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Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors
... The organization of the paper is as follows: In the second section, we introduce some definitions, tensor models, and the Marchenko-Pastur distribution from random matrix theory. The third section is devoted to present ...
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Penalizing Unfairness in Binary Classification
... Despite these theoretical challenges, learning fair classi- fiers remains an important, practical problem that must be addressed on real data—decisions must be taken, and trade- offs must be made. To this end, there have ...
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The evaluation of binary classification tasks in economical prediction
... In the area of economical research, much atten- tion has been paid to development and improve- ment of many prediction methods and models so far. One of the typical tasks being solved is the bank- ruptcy and fi nancial ...
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<p>The Binary Classification Of Chronic Diseases</p>
... In addition, the model rationalizes why immune-modulating drugs, which are effective in cancer, are also effective in “ high Treg ” viral infections, while corticosteroids, which are gen[r] ...
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Datamining Application for the Prediction of Binary Classification Problems
... involve classification, aggregation, clustering etc. By using classification we can group the data based on a common ...in classification to achieve greatest ...elevated classification ...
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