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Augmenting cost-SVM with gaussian mixture models for imbalanced classification

Miao He, Teresa Wu, Alvin Silva, Dianna-Yue Zhao, Wei Qian
2015 Artificial intelligence research  
Experimental results on eleven benchmark datasets and one medical imaging dataset show the effectiveness of CSG in dealing with imbalanced classification problems.  ...  to enforce cost-sensitivity.  ...  Conclusion and discussion In this research, we propose a model fusion based approach integrating cSVM with GMM for the imbalanced classification problem.  ... 
doi:10.5430/air.v4n2p93 fatcat:wszcgn6sxzeqloaivlh5xhqkbu

Classification Imbalanced Data Sets: A Survey

Shrouk El-Amir, Heba El-Fiqi
2019 International Journal of Computer Applications  
Unbalanced data, a snag often found in real-world applications, can seriously adversely affect machine learning algorithms ' classification efficiency.  ...  In order to face the imbalanced data sets snag, we should rebalance them artificially through machine learning classifiers by oversampling and/or undersampling.  ...  The different approaches for facing the imbalanced data issue in the literature can be divided into three groups: data level, algorithm level and cost-sensitive approaches [1] .  ... 
doi:10.5120/ijca2019919682 fatcat:xaquuseswfho3pgng7z3gc33wy

An Optimized Cost-Sensitive SVM for Imbalanced Data Learning [chapter]

Peng Cao, Dazhe Zhao, Osmar Zaiane
2013 Lecture Notes in Computer Science  
This paper presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive SVM directly to improve the performance of classification  ...  Class imbalance is one of the challenging problems for machine learning in many real-world applications.  ...  classification performance of the cost sensitive SVM on different datasets.  ... 
doi:10.1007/978-3-642-37456-2_24 fatcat:rpms3hwrhrf3bgnh25jjdtgyh4

Cost-sensitive decision tree ensembles for effective imbalanced classification

Bartosz Krawczyk, Michał Woźniak, Gerald Schaefer
2014 Applied Soft Computing  
Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification.  ...  , and hence to represent a useful and effective approach for dealing with imbalanced datasets.  ...  Techniques that address the problems associated with imbalanced datasets can in general be divided into three groups [33] : • Data level approaches work, in a pre-processing stage, directly on the data  ... 
doi:10.1016/j.asoc.2013.08.014 fatcat:b5lqccytfjcg7jq5yy64yrfaze

A Comprehensive Analysis of Handling Imbalanced Dataset

2021 International Journal of Advanced Trends in Computer Science and Engineering  
in machine learning, such as Data Sampling Approach, Cost sensitive learning approach and Ensemble Approach.  ...  the class distribution is highly imbalanced), hence imbalance class distribution requires special consideration, and for this purpose we dealt extensively on handling and solving imbalanced class problem  ...  Cost-Sensitive Learning Approach: Is a different approach used for solving class imbalance problems in machine learning, In Inductive Learning approach to classification, all the classification algorithms  ... 
doi:10.30534/ijatcse/2021/031022021 fatcat:7wjm4keqvberpfkp6r243eo3n4

Learning from a Class Imbalanced Public Health Dataset: a Cost-based Comparison of Classifier Performance

Rohini R Rao, Krishnamoorthi Makkithaya
2017 International Journal of Electrical and Computer Engineering (IJECE)  
The objective of this work is to identify the best classifiers for class imbalanced health datasets through a cost-based comparison of classifier performance.  ...  Classifiers built using the random under-sampled dataset showed a dramatic drop in costs and high classification accuracy.  ...  Veena Kamath, Professor, Department of Community Medicine, KMC, Manipal, for extending us her subject expertise.  ... 
doi:10.11591/ijece.v7i4.pp2215-2222 fatcat:wqxmcq4pfnapvlsxc6rcyxihfq

A Framework of Rebalancing Imbalanced Healthcare Data for Rare Events' Classification: A Case of Look-Alike Sound-Alike Mix-Up Incident Detection

Yang Zhao, Zoie Shui-Yee Wong, Kwok Leung Tsui
2018 Journal of Healthcare Engineering  
In this study, we develop a framework for learning healthcare data with imbalanced distribution via incorporating different rebalancing strategies.  ...  However, imbalanced class distribution in many practical datasets greatly hampers the detection of rare events, as most classification methods implicitly assume an equal occurrence of classes and are designed  ...  Acknowledgments This work was supported in part by the RGC Theme-Based Research Scheme (TBRS) (no.  ... 
doi:10.1155/2018/6275435 pmid:29951182 pmcid:PMC5987310 fatcat:hlbhsjszkjdgnm43fx36xq4s7u

A Novel Method for Credit Scoring based on Cost-sensitive Neural Network Ensemble

Wirot Yotsawat, Pakaket Wattuya, Anongnart Srivihok
2021 IEEE Access  
Alejo et al. (2013) [39] improved MLP predictive performance using cost-sensitive learning based on a single classifier.  ...  [38] proposed a cost-sensitive boosted tree model to improve the performance of classification model.  ... 
doi:10.1109/access.2021.3083490 fatcat:x4moo2t4qjcnxmi6sp72bkth6q

Learning from Imbalanced Data in Classification

2020 International journal of recent technology and engineering  
Based on the experiment conducted on one dataset it is found that ensemble technique along with other data-level methods gives good results.  ...  Imbalanced data learning is a research area and day by day development is going on.  ...  A good understanding is required for the modified learning algorithm. The cost-sensitive approach is the most popular one.  ... 
doi:10.35940/ijrte.e6286.018520 fatcat:u62u7ylyjrh7xiww3u7agnljwa

Performance analysis of cost-sensitive learning methods with application to imbalanced medical data

Ibomoiye Domor Mienye, Yanxia Sun
2021 Informatics in Medicine Unlocked  
Furthermore, recent research has suggested a high correlation between cost-sensitive learning and imbalanced classification; hence, the conceptual frameworks and algorithms utilized for cost-sensitive  ...  This research aims to provide a general overview of the imbalanced classification problem and ML algorithms suitable for such classification problems focusing on medical data.  ...  J o u r n a l P r e -p r o o f Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence  ... 
doi:10.1016/j.imu.2021.100690 fatcat:fbxsyvzv2zdvfcn6mina5nxdme

An Investigation on Topic Maps Based Document Classification with Unbalance Classes

Maher Baloch, Muhammad Rafi
2015 Journal of Independent Studies and Research - Computing  
Classification of imbalanced data has become a widespread problem due to the fact that the most real world datasets are imbalanced.  ...  In order to measure of topic-map based representation for classification under imbalance data, authors compare three representations: Bag-of-Words, Phrases and Topic terms for three approaches (i) under-sampling  ...  The authors would like to thanks Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology for the research support.  ... 
doi:10.31645/jisrc/(2015).13.1.0007 fatcat:nerrh7jw7becdm7gezsqcz66om

On the Classification of Imbalanced Datasets

Arun KumarM.N, H. S. Sheshadri
2012 International Journal of Computer Applications  
This paper does not mention all the available research solutions, but try to give a clear picture of imbalanced data set classification problem and present a brief review of existing solutions on this  ...  Here, we consider binary classification problem on imbalanced data sets.  ...  In addition to [28] [29] [30] , several other undersampling approaches are available in the literature. On the Classification of Imbalanced Datasets B.  ... 
doi:10.5120/6280-8449 fatcat:zl776vai6bgelcgnzjfodpprxu

A Survey on Imbalanced Data Handling Techniques for Classification

2021 International Journal of Emerging Trends in Engineering Research  
minority class for a classifier, such dataset is termed an imbalanced dataset.  ...  A survey on various techniques proposed by the researchers for handling imbalanced data has been presented and a comparison of the techniques based on f-measure has been identified and discussed.  ...  They had also optimized cost ratio (cost matrix) locally and used cost-sensitive learning for improving classifier performance.  ... 
doi:10.30534/ijeter/2021/089102021 fatcat:hxwoz3qrbngdfov62ndkdeykfa

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Nazeeh Ghatasheh, Hossam Faris, Ismail AlTaharwa, Yousra Harb, Ayman Harb
2020 Applied Sciences  
This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets.  ...  ., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples.  ...  The overall results of using cost-sensitive approaches show classification performance improvement in comparison with classical classification algorithms.  ... 
doi:10.3390/app10072581 fatcat:dikgu42nnnc3jc4j2z55ik6htu

Cost Sensitive Ranking Support Vector Machine for Multi-label Data Learning [chapter]

Peng Cao, Xiaoli Liu, Dazhe Zhao, Osmar Zaiane
2017 Advances in Intelligent Systems and Computing  
In this paper, we study the problem of multi-label imbalanced data classification and propose a novel solution, called CSRankSVM (Cost sensitive Ranking Support Vector Machine), which assigns a different  ...  Empirical studies on popular benchmark datasets with various imbalance ratios of labelsets demonstrate that the proposed CSRankSVM approach can effectively boost classification performances in multi-label  ...  The ImbalR of multi-label datasets Cost sensitive RankSVM Current cost-sensitive learning research has been focused on binary or multi-class classification, but never yet on multi-label classification  ... 
doi:10.1007/978-3-319-52941-7_25 fatcat:l3lx4ntuafdddiudcdetikcucm
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