Cost-sensitive support vector machines

Iranmehr, Arya; Masnadi-Shirazi, Hamed; Vasconcelos, Nuno

doi:10.1016/j.neucom.2018.11.099

Cited by 157 publications

(60 citation statements)

References 20 publications

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“…Precisely, cost-sensitive learning methods introduce a cost of misclassification to weigh differently the classification errors in different classes [78]. In particular, in this paper we applied the cost-sensitive svm, also known as ''biased penalties svm'' [81]. More in detail, in the framework of the svm optimization problem, two regularization parameters, θ + and θ − , were introduced to be able to adjust the cost of misclassification of negative and positive samples.…”

Section: ) Knn Classifiers and Support Vector Machines For Predictionmentioning

confidence: 99%

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Human Digital Twin for Fitness Management

et al. 2020

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Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

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Section: ) Knn Classifiers and Support Vector Machines For Predictionmentioning

confidence: 99%

“…c) the data unbalancing. To cope with this problem we test two different techniques; the first one is the training set subsampling described in Subsection b), while the second is the use of cost sensitive support vector machines [81] (see Subsection III.A.3). d) the choice of the number of neighbors to be considered.…”

Section: ) Missing Data Imputationmentioning

confidence: 99%

Human Digital Twin for Fitness Management

et al. 2020

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“…Machine learning (ML) has been strongly applied to solve supervised and unsupervised problems. ML deploys different algorithms, such as online learning, multi-task learning and supervised algorithms, including rule based [8,9], function based [10,11], lazy [12], and bootstrap [13]. Some of them are used to transform data, special example would be dimension reduction for optimization, some to build classifiers like supervised algorithms, others for prediction like regression, etc.…”

Section: Machine Learning : Challenges and Drawbacksmentioning

confidence: 99%

An Introduction to Advanced Machine Learning: Meta-Learning Algorithms, Applications, and Promises

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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In [1,2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?.

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“…(10) Equation 6is the Euclidean distance between samples. 7represents the sum of Euclidean distances from one sample to other samples, and (8) and (9) represent the first initial cluster center and the second initial cluster center, respectively. Specifically, (10) is a recursive formula for the initial cluster center and 3 jk , k represents the number of clusters.…”

Section: Optimize Cluster Center Selectionmentioning

confidence: 99%

“…In addition to the resampling method, some scholars have proposed some effective methods from the algorithm level. In [9] proposed an cost-sensitive learning algorithm CS-SVM, which integrates the idea of cost-sensitive learning into the SVM classifier, and optimizes the maximum separation surface of SVM for different classes. The samples are assigned different penalty factors, which improve the SVM's ability to classify unbalanced data.…”

Section: Introductionmentioning

confidence: 99%

Clustering Center Optimization under-Sampling Method for Unbalanced Data

Li¹,

Zhuang²

2020

JSW

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When the number of data in one class is significantly larger or less than the data in other class, under learning algorithm for classification, a problem of learning generalization occurs to the specific class and this is called imbalanced data problem. In this paper, a method of under-sampling based on the optimization cluster center selection (BCUSM) is proposed. First of all, the cluster center selection of K-means clustering algorithm is optimized, the initial cluster center is obtained by calculation, instead of random selection. The optimized method is called OICSK-means. And then use it to cluster the negative samples by setting the same number of clusters as positive samples. According to the cosine similarity, select the most similar samples from each cluster with cluster centers as the negative training samples, and a new training set is established with the positive samples. Finally, training with a new training set. This work selected some data from the UCI database of the University of California, Irvine, and used the support vector machine (SVM) classifier for experimental simulation, and compared the classification effects of this method with other four methods such as synthetic oversampling method (SMOTE). The experimental results demonstrate that the BCUSM has certain effectiveness.

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Cost-sensitive support vector machines

Cited by 157 publications

References 20 publications

Human Digital Twin for Fitness Management

Human Digital Twin for Fitness Management

An Introduction to Advanced Machine Learning: Meta-Learning Algorithms, Applications, and Promises

Clustering Center Optimization under-Sampling Method for Unbalanced Data

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