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Binary classification
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In this paper, we propose an effective AMC using deep learning (DL) for flexible and adaptive OFDM-based optical networks. The proposed DL-based AMC is able to classify four typical modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), 8-PSK, and 16- quadrature amplitude modulation (QAM) in dynamic network conditions.
6p
vithomson
02-07-2024
0
0
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Lecture Artificial intelligence: Binary classifiers for multi-class classification problems. This lecture provides students with content including: classification; binary classification; multi-class classification; binary classifiers; support vector machines; perceptron; logistic regression;... Please refer to the detailed content of the lecture!
12p
codabach1016
03-05-2024
4
0
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This paper presents research on detecting fake news based on news content and social context approach using machine learning. First of all, we analyze related concepts, methods of detecting fake news. Next, we model this task as a binary classification problem, representing news content and social context as feature vectors.
12p
vigojek
02-02-2024
6
2
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Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or “cutpoint,” to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification.
11p
vighostrider
25-05-2023
3
2
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This paper proposes an integration of one-againstone (OAO) strategy and support vector machines (SVM) to diagnose multiple faults of steel plates. The OAO is adopted to address multi-classification tasks in the binary SVM (i.e, OAOSVMs). The performance of the proposed model is compared with that of optimization algorithm-based SVM.
4p
vispyker
16-11-2022
6
1
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In this paper, we propose a new Improvement Least Square - Support Vector Machine (called ILS-SVM) for binary classification problems with a class-vs-cluster strategy. Experimental results show that the ILS-SVM training time is faster than that of TSVM, and the ILS-SVM accuracy is better than LSTSVM and TSVM in most cases.
6p
viplato
05-04-2022
17
1
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In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classi¯cation.
25p
redemption
20-12-2021
24
0
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The analysis on the location and dispersion statistics of the performance measures was further enlightened on: (i) the effect of a resampling method on the performance of LDA, and (ii) the enhancement in the learning fairness of LDA on objects regardless of sample size, hence reducing the effect of the curse of class imbalance.
20p
spiritedaway36
28-11-2021
20
4
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To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet.
13p
vijeeni2711
24-07-2021
11
0
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The presented algorithm has shown the accuracy of classification of the sequences specified in the work 0.98 and can be implemented in DLP systems to prevent the transmission of information in encrypted or compressed form.
8p
chauchaungayxua12
11-05-2021
24
4
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This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.
14p
nguaconbaynhay11
07-04-2021
14
2
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The paper depicts complete study about the second method with some proposed algorithms. It focuses mainly on binary classification with kNN and SVM for imbalanced data. Experiments and comparisons among related methods will confirm pros and coin of each method with respect to performance accuracy and time consumption.
20p
viguam2711
11-01-2021
10
2
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The traditional Tönnis Classification System has inherent drawbacks as it is vulnerable to the subjectivity of a four-grade system. A two-grade classification could potentially be more reliable.
9p
vimariana2711
22-12-2020
13
0
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Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known.
11p
viwyoming2711
16-12-2020
11
0
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Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups.
12p
vioklahoma2711
19-11-2020
12
4
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Selecting a parsimonious set of informative genes to build highly generalized performance classifier is the most important task for the analysis of tumor microarray expression data. Many existing gene pair evaluation methods cannot highlight diverse patterns of gene pairs only used one strategy of vertical comparison and horizontal comparison, while individual-gene-ranking method ignores redundancy and synergy among genes.
16p
vioklahoma2711
19-11-2020
14
2
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With modern methods in biotechnology, the search for biomarkers has advanced to a challenging statistical task exploring high dimensional data sets. Feature selection is a widely researched preprocessing step to handle huge numbers of biomarker candidates and has special importance for the analysis of biomedical data. Such data sets often include many input features not related to the diagnostic or therapeutic target variable.
21p
vicolorado2711
22-10-2020
17
0
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Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions, and (b) the need for highly interpretable glass-box models.
27p
vicolorado2711
22-10-2020
8
0
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After completing this chapter, students will be able to: Classification of entities, attributes on relationships, structural constraints, multiplicity, binary/complex relationships, structural constraints, multiplicity, connection traps.
20p
thuongdanguyetan03
18-04-2020
12
1
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In terms of ideas, SVM uses tricks to map the original dataset to more dimensional spaces. Once mapped to a multidimensional space, SVM will review and select the most suitable superlattice to classify that data set.
9p
kequaidan3
11-03-2020
10
1
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