A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Soft Methodology for Cost-and-error Sensitive Classification
[article]
2017
arXiv
pre-print
Preserved Fulltext
The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification ...
Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. ...
The proposed methodology, soft cost-sensitive classification, takes both the cost and the error (or the weighted error) into account by a multicriteria optimization problem. ...
arXiv:1710.09515v1
fatcat:zwk3exijszbf7ng77elkj37obe
A simple methodology for soft cost-sensitive classification
2012
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification ...
Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. ...
The authors thank Yao-Nan Chen, Ku-Chun Chou, Chih-Han Yu and the anonymous reviewers for valuable comments. ...
doi:10.1145/2339530.2339555
dblp:conf/kdd/JanWLL12
fatcat:vzgmkndv7fbrpmxviflkouv6by
A New Cost Function for Binary Classification Problems Based on the Distributions of the Soft Output for Each Class
2007
Neural Networks (IJCNN), International Joint Conference on
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
This cost function aims at reducing the probability of classification error by reducing the overlap between distributions of the soft output for each class. ...
This paper proposes a new cost function for supervised training of neural networks in binary classification applications. ...
TIC2003-02602, and project 'MONIN', id. TEC2006-13514-C02-01), and Comunidad de Madrid (project 'PRO-MULTIDIS-CM', id. S0505/TIC/0223). ...
doi:10.1109/ijcnn.2007.4371174
dblp:conf/ijcnn/LazaroLAF07
fatcat:qzrqtolnznfprd2huwnoqtcbky
Subspace electrode selection methodology for the reduction of the effect of uncertain conductivity values in the EEG dipole localization: a simulation study using a patient-specific head model
2012
Physics in Medicine and Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper presents a novel numerical methodology for the increase of accuracy of the EEG dipole source localization problem. ...
We redefine the traditional cost function associated with the EEG inverse problem and introduce a selection procedure of EEG potentials. ...
Acknowledgments The authors wish to thank Hans Hallez and Bertrand Yitembe, and gratefully acknowledge the financial support of GOA07/GOA/006 and IUAP P6/21. ...
doi:10.1088/0031-9155/57/7/1963
pmid:22421525
fatcat:7n4ogehj7vfobhjs42suh72dda
Analyzing the instructions vulnerability of dense convolutional network on GPUS
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation ...
However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. ...
CNN model DenseNet201 was used
GPUs with logic units more sensitive to
soft errors was used. ...
doi:10.11591/ijece.v11i5.pp4481-4488
fatcat:sjvfjemysrf2nmfodixdwwvz4i
Analyzing the Impact of Soft Errors in Deep Neural Networks on GPUsfrom Instruction Level
2020
WSEAS transactions on systems and control
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this work, we propose a comprehensive methodology to analyze the reliability of object detection and classification algorithms run on GPUs from the lowest (instruction) evaluation level. ...
Moreover, we show that YOLO is more sensitive to the changes caused by soft errors than ResNet. Also, ResNet depends on the input image in its reliability, while YOLO tends to be independent. ...
through SASSIFI fault injection to char acterize the error resilience behaviors of im age classification and object detection systems against soft errors. • Error criticality measurement to classify instruc ...
doi:10.37394/23203.2020.15.70
fatcat:cchrrxeozfduxpoccts7pxz2ie
SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms
2002
IEEE Transactions on Instrumentation and Measurement
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
The method, based on a harmonic analysis, allows for selecting the most suitable test input stimuli and nodes by means of a global sensitivity approach efficiently carried out by randomized algorithms. ...
Diagnosis, obtained by comparing signatures measured at the test nodes with those contained in a fault dictionary, allows for sub-systems testing and fault isolation within the circuit. ...
The classification error improvement in fault detection and isolation with respect to the optimized procedure suggested in [14] is around 2%, which represents a relevant increment due to the cost of ...
doi:10.1109/tim.2002.806004
fatcat:jwip5w4w2nghvjg5lvpwjxateq
T1-T2 WEIGHTED MR IMAGE COMPOSITION AND CATALOGUING OF BRAIN TUMOR USING REGULARIZED LOGISTIC REGRESSION
2016
Jurnal Teknologi
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Magnetic Resonance Imaging (MRI) T1-weighted and T2-weighted images provides suitable variation of contrast between the different soft tissues of the brain which is suitable for detecting the brain tumor ...
The proposed method uses the Regularized Logistic Regression (RLR) for the efficient cataloguing of brain tumor in which it achieves an effective accuracy rate of 96%, specificity rate of 86% and sensitivity ...
It is interestingly noted that the proposed methodology, WATS+ RLR was able to achieve 96% of accuracy, 86 % of specificity and 97% of sensitivity when executed. ...
doi:10.11113/jt.v78.5930
fatcat:kj6ncxxagfhvjokxzekon6v5nu
Entropy-Based Modeling for Estimating Soft Errors Impact on Binarized Neural Network Inference
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
These models can be used for evaluating the error-resiliency magnitude of NN topology before adopting them in the safety-critical applications. ...
In this paper, we present the relatively-accurate statistical models to delineate the impact of both undertaken SEU and MBU across layers and per each layer of the selected NN. ...
To optimize the architecture design for ML/DL accelerators, a broad spectrum of methodologies have been proposed at both software and hardware levels. ...
arXiv:2004.05089v2
fatcat:j6j3gcjarrffzgecjqndfmg76q
Rejection Threshold Optimization using 3D ROC Curves: Novel Findings on Biomedical Datasets
2021
International Journal of Intelligent Systems and Applications in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Although optimization of error-reject trade-off has been widely investigated, it is shown that error rate itself is not an appropriate performance measure, when misclassification costs are unequal or class ...
Considering classification with reject option, we need to represent the tradeoff between TP, FP and rejection rates. ...
He obtained Bayesian optimal errorreject characteristics by using cost sensitive classification and changing the decision threshold. ...
doi:10.18201/ijisae.2021167933
fatcat:zgk5n3eoavfyvoxnmogxnkrsuy
An Adaptive Resource Allocating Neuro-Fuzzy Inference System with Sensitivity Analysis Resource Control
[chapter]
2009
IFIP Advances in Information and Communication Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems. ...
Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors. ...
Introduction The guiding principle of soft computing is to exploit the tolerance for imprecision by devising methods of computation that lead to an acceptable solution at low cost [1] . ...
doi:10.1007/978-1-4419-0221-4_59
fatcat:xj37puf7o5fpzokd422mrkrpcy
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
2020
Journal of Soft Computing Paradigm
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
campaigns, research process and the scientific endeavors. ...
Multitudes of methods were devised to improve the accuracy in the classification to devour an enhanced performance in terms of faster convergence speed. ...
, Sensitivity and Error Rate Where the accuracy is measured using the[ 𝐴 𝐵 ⁄ ], sensitivity is estimated applying[ 𝑡 𝑝 𝐶 ⁄ ], and applying[ 𝑡 𝑛 𝐷 ⁄ ], for specificity whereas 1-[ 𝐴 𝐵 ⁄ ] for ...
doi:10.36548/jscp.2020.1.004
fatcat:cgkxov3uyzeh7og5ljkknom4jy
Support Vector Machines Classification on Class Imbalanced Data: A Case Study with Real Medical Data
2021
Journal of Data Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We compare the standard (or classic) SVM (C SVM) with resampling methods and a cost sensitive method, called Two Cost SVM (TC SVM), which constitute widely accepted strategies for imbalanced datasets and ...
In our comparative study we use a cost sensitive learning technique proposed by Veropoulos et al. (1999) called "TC SVM" due to the fact that it uses two costs for the two different classes. ...
Acknowledgements The authors would like to thank the Editor and the Referee for their valuable comments and suggestions that resulted in improving the quality of presentation of this manuscript. ...
doi:10.6339/jds.201410_12(4).0009
fatcat:4s3xqbh5h5g7xhdfhj6qrf445i
Coronary Artery Disease Diagnosis Using Optimized Adaptive Ensemble Machine Learning Algorithm
2020
International Journal of Bioscience Biochemistry and Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this study, we have proposed a novel Self Optimized and Adaptive Ensemble Machine Learning Algorithm for the diagnosis of CAD. ...
Cardiovascular diseases (CVD) involving the heart and blood vessels are reported as the leading causes of mortality worldwide. ...
Author Contributions All authors had conducted the research and approved the final version. ...
doi:10.17706/ijbbb.2020.10.1.58-65
fatcat:bfprjzqrmravlhi7lcwns4nhnu
Intelligent Decision Support System for Breast Cancer
[chapter]
2010
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
In this paper we develop an integrated expert system for diagnosis, prognosis and prediction for breast cancer using soft computing techniques. ...
Breast cancer database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository. ...
Deshmukh, Director ABV-IIITM, Gwalior, India for encouraging and providing facilities to carry out this research work. This work is sponsored by ABV-IIITM Gwalior. ...
doi:10.1007/978-3-642-13498-2_46
fatcat:i6atgfy3vbhlfbnu3nryssb5i4
« Previous
Showing results 1 — 15 out of 35,402 results