scholarly journals A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Zhenbing Liu ◽  
Chunyang Gao ◽  
Huihua Yang ◽  
Qijia He
Keyword(s):  
Sparse Representation ◽  
Classification Accuracy ◽  
Class Imbalance ◽  
Misclassification Rate ◽  
Data Sets ◽  
Class Imbalance Problem ◽  
Misclassification Cost ◽  
Practical Applications ◽  
Imbalance Problem ◽  
Positive Class

Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC) for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate, and negative class misclassification rate. In addition, we sampled test samples and training samples with different imbalance ratio and use F-measure, G-mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM.

Image Classifying Based on Cost-Sensitive Layered Cascade Learning

2014 ◽  
Vol 701-702 ◽  
pp. 453-458
Author(s):  
Feng Huang ◽  
Yun Liang ◽  
Li Huang ◽  
Ji Ming Yao ◽  
Wen Feng Tian
Keyword(s):  
Image Classification ◽  
Class Imbalance ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Class Imbalance Problem ◽  
Data Set ◽  
Misclassification Cost ◽  
Imbalance Problem ◽  
Specific Category ◽  
The Cost

Image Classification is an important means of image processing, Traditional research of image classification usually based on following assumptions: aiming for the overall classification accuracy, sample of different category has the same importance in data set and all the misclassification brings same cost. Unfortunately, class imbalance and cost sensitive are ubiquitous in classification in real world process, sample size of specific category in data set may much more than others and misclassification cost is sharp distinction between different categories. High dimension of eigenvector caused by diversity content of images and the big complexity gap between distinguish different categories of images are common problems when dealing with image Classification, therefore, one single machine learning algorithms is not sufficient when dealing with complex image classification contains the above characteristics. To cure the above problems, a layered cascade image classifying method based on cost-sensitive and class-imbalance was proposed, a set of cascading learning was build, and the inner patterns of images of specific category was learned in different stages, also, the cost function was introduced, thus, the method can effectively respond to the cost-sensitive and class-imbalance problem of image classifying. Moreover, the structure of this method is flexible as the layer of cascading and the algorithm in every stage can be readjusted based on business requirements of image classifying. The result of application in sensitive image classifying for smart grid indicates that this image classifying based on cost-sensitive layered cascade learning obtains better image classification performance than the existing methods.

scholarly journals A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem

2019 ◽  
Vol 24 (2) ◽  
pp. 104-110
Author(s):  
Duygu Sinanc Terzi ◽  
Seref Sagiroglu
Keyword(s):  
Big Data ◽  
Class Imbalance ◽  
Area Under The Curve ◽  
Data Sets ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
The Common ◽  
Public Datasets ◽  
Distributed Cluster

Abstract The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling for Imbalanced Big Data (DIBID). The design aims at modifying the existing dataset to increase the classification success. Within the study, DIBID has been implemented on public datasets under two strategies. The first strategy has been designed to present the success of the model on data sets with different imbalanced ratios. The second strategy has been designed to compare the success of the model with other imbalanced big data solutions in the literature. According to the results, DIBID outperformed other imbalanced big data solutions in the literature and increased area under the curve values between 10 % and 24 % through the case study.

Pulsar candidate classification using generative adversary networks

2019 ◽  
Vol 490 (4) ◽  
pp. 5424-5439 ◽  
Author(s):  
Ping Guo ◽  
Fuqing Duan ◽  
Pei Wang ◽  
Yao Yao ◽  
Qian Yin ◽  
...  
Keyword(s):  
Class Imbalance ◽  
Feature Learning ◽  
Computer Experiments ◽  
Support Vector ◽  
Data Sets ◽  
Class Imbalance Problem ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Imbalance Problem

ABSTRACT Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments, the volume and rate of data acquisition have grown exponentially. This development necessitates a focus on artificial intelligence (AI) technologies that can mine large astronomical data sets. Automatic pulsar candidate identification (APCI) can be considered as a task determining potential candidates for further investigation and eliminating the noise of radio-frequency interference and other non-pulsar signals. As reported in the existing literature, AI techniques, especially convolutional neural network (CNN)-based techniques, have been adopted for APCI. However, it is challenging to enhance the performance of CNN-based pulsar identification because only an extremely limited number of real pulsar samples exist, which results in a crucial class imbalance problem. To address these problems, we propose a framework that combines a deep convolution generative adversarial network (DCGAN) with a support vector machine (SVM). The DCGAN is used as a sample generation and feature learning model, and the SVM is adopted as the classifier for predicting the label of a candidate at the inference stage. The proposed framework is a novel technique, which not only can solve the class imbalance problem but also can learn the discriminative feature representations of pulsar candidates instead of computing hand-crafted features in the pre-processing steps. The proposed method can enhance the accuracy of the APCI, and the computer experiments performed on two pulsar data sets verified the effectiveness and efficiency of the proposed method.

scholarly journals Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianhong Yan ◽  
Suqing Han
Keyword(s):  
Learning Community ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Data Sets ◽  
Class Imbalance Problem ◽  
Imbalanced Data Sets ◽  
Imbalance Data ◽  
Imbalance Problem ◽  
Stacked Generalization ◽  
Model Generalization

Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem.

scholarly journals An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jing Bian ◽  
Xin-guang Peng ◽  
Ying Wang ◽  
Hai Zhang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Network Security ◽  
Classification Accuracy ◽  
Large Scale ◽  
Class Imbalance ◽  
Evaluation Function ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Chaos Genetic Algorithm

In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs) and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 andk-nearest neighbor (KNN). The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

Improvising Balancing Methods for Classifying Imbalanced Data

2021 ◽  
Vol 9 (9) ◽  
pp. 1535-1543
Author(s):  
Himani Tiwari
Keyword(s):  
Learning Community ◽  
Sampling Methods ◽  
Evaluation Criteria ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Simulated Data ◽  
Data Sets ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Under Sampling

Abstract: Class Imbalance problem is one of the most challenging problems faced by the machine learning community. As we refer the imbalance to various instances in class of being relatively low as compare to other data. A number of over - sampling and under-sampling approaches have been applied in an attempt to balance the classes. This study provides an overview of the issue of class imbalance and attempts to examine various balancing methods for dealing with this problem. In order to illustrate the differences, an experiment is conducted using multiple simulated data sets for comparing the performance of these oversampling methods on different classifiers based on various evaluation criteria. In addition, the effect of different parameters, such as number of features and imbalance ratio, on the classifier performance is also evaluated. Keywords: Imbalanced learning, Over-sampling methods, Under-sampling methods, Classifier performances, Evaluationmetrices

Combating the class imbalance problem in sparse representation learning

2018 ◽  
Vol 35 (2) ◽  
pp. 1865-1874
Author(s):  
Ying Ma ◽  
Xiatian Zhu ◽  
Shunzhi Zhu ◽  
Keshou Wu ◽  
Yuming Chen
Keyword(s):  
Sparse Representation ◽  
Class Imbalance ◽  
Representation Learning ◽  
Class Imbalance Problem ◽  
Imbalance Problem

scholarly journals Sampling Techniques to Overcome Class Imbalance in a Cyberbullying Context

2019 ◽  
Vol 3 (1) ◽  
pp. 21
Author(s):  
David Colton ◽  
Markus Hofmann
Keyword(s):  
Class Imbalance ◽  
Sampling Techniques ◽  
Compromise Solution ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Imbalance Problem ◽  
Positive Class ◽  
Negative Class ◽  
Significant Class ◽  
Machine Learning Models

<div data-canvas-width="705.3003252350338">The majority of datasets suffer from class imbalance where samples of a dominant class significantly outnumber the samples available for the minority class that is to be detected. Prediction and classification machine learning models work best when there are roughly equal numbers of each class type. This paper explores sampling techniques that can be used to overcome this class imbalance problem in a cyberbullying context. A newly classified cyberbullying dataset, including detailed descriptions of the criteria used in its classification, was used to examine the feasibility of applying text mining techniques, to automate the detection of cyberbullying text when the dataset shows a significant class imbalance between the positive, cyberbullying, sample and the negative, not cyberbullying, samples. In this paper, we will investigate if oversampling the minority positive class or undersampling the majority negative class affects the performance of a prediction model. A compromise solution where the positive class is partially oversampled, and the negative class is partially undersampled is also examined. Although not strictly a class imbalance solution, sampling using the most frequently observed features was also explored.</div><p> </p>

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