Results 31 to 40 of about 693,025 (273)
Multi-class Nearest Neighbour Classifier for Incomplete Data Handling [PDF]
, 2015 The basic nearest neighbour algorithm has been designed to work with complete data vectors. Moreover, it is assumed that each reference sample as well as classified sample belong to one and the only one class. In the paper this restriction has been dismissed.
Bartosz A. Nowak +3 more
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Wasit Journal of Computer and Mathematics Science, 2021 Network Intrusion Detection System (NIDS) detects normal and malicious behavior by analyzing network traffic, this analysis has the potential to detect novel attacks especially in IoT environments. Deep Learning (DL)has proven its outperformance compared
Rawaa Ismael Farhan +2 more
doaj +1 more source
A Classifier Capable of Handling Incomplete Data Set
The Journal of the Korean Institute of Information and Communication Engineering, 2010 This paper introduces a classification algorithm which can be applied to a learning problem with incomplete data sets, missing variable values or a class value. This algorithm uses a data expansion method which utilizes weighted values and probability techniques. It operates by extending a classifier which are considered to be in the optimal projection
Jong-Chan Lee, Won-Don Lee
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FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes
IEEE Access, 2021 With the increased use of machine learning algorithms to make decisions which impact people’s lives, it is of extreme importance to ensure that predictions do not prejudice subgroups of the population with respect to sensitive attributes such as ...
Teresa Salazar +3 more
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Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI
Journal of Information Systems Engineering and Business Intelligence, 2022 Background: Brain tumour categorisation can be assisted with computer-aided diagnostic (CAD) for medical applications. Biopsies to classify brain tumours can be costly and time-consuming. Radiologists may also misclassify brain tumour types when handling
Hafiza Akter Munira, Md Saiful Islam
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Ensemble Classification Algorithm for Imbalanced Data Combined with Local Area Density
Jisuanji kexue yu tansuo, 2020 Oversampling method is one of the effective ways to deal with imbalance classification problems. This paper focuses on the problems of reducing sampling result faced by the oversampling methods based on SMOTE(synthetic minority oversampling technique) in
YANG Hao, CHEN Hongmei
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Random Partition Based Adaptive Distributed Kernelized SVM for Big Data
IEEE Access, 2022 In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster.
Amrit Pal +5 more
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Effect of Data Preparation on Machine Learning Models for Diabetes Prediction
Engineering ProceedingsThis paper examines how data preparation affects machine-learning classifiers for diabetes-risk prediction using the Pima Indians Diabetes Database. Three preprocessing methods are considered: imputing invalid zeros, handling outliers, and data scaling ...
Goran Martinović +3 more
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Robust Inference via Generative Classifiers for Handling Noisy Labels
CoRR, 2019 Accepted in ICML ...
Kimin Lee +5 more
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Introduction Peritoneal dialysis (PD) is an established home‐based kidney replacement therapy (KRT), but its uptake remains low in Japan. We evaluated whether individualized education in a dedicated outpatient clinic was associated with the initiation of PD.
Yasuko Ito +7 more
wiley +1 more source