Results 171 to 180 of about 39,918 (213)

Supervised Microalgae Classification in Imbalanced Dataset

2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 2016
Microalgae are unicellular organisms that have physical characteristics such as size, shape or even the present structures. Classifying them manually may require great effort from experts since thousands of microalgae can be found in a small sample of water. Furthermore, the manual classification is not a trivial operation.
Iago Correa, Paulo Drews, Marcio Silva De Souza, Virginia Maria Tavano   +3 more
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To improve classification of imbalanced datasets

2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
The task of accurately predicting the target class for each case in the data is called classification of data in data mining. Classification of balanced data set is fairly simple and easy to perform but it becomes difficult when the data is not balanced.
Pratyusha Shukla, Kiran Bhowmick
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Improved SMOTE algorithm for imbalanced dataset

2020 Chinese Automation Congress (CAC), 2020
When applying traditional classifiers to imbalanced dataset, the result might be bias towards the majority class, which leads to poor performance of classifiers. Synthetic Minority Oversampling Technique(SMOTE) is a popular algorithm to improve the classifier’s performance through generating new minority samples and making dataset balanced.
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Generating images for imbalanced dataset problem

2017 36th Chinese Control Conference (CCC), 2017
Imbalanced dataset problem may occur when the number of instances of a certain class is much lower than others, resulting in a drop in the classification result of minority class. We propose the method of generating images from 3D modeling by some softwares to get enough images of minority class and supplement the dataset to re-balance it. Several deep
Yingying Qin, Wenjie Chen, Jie Chen
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Imbalanced Dataset Problem in Sentiment Analysis

2019 4th International Conference on Computer Science and Engineering (UBMK), 2019
In this study, the problems caused by unbalanced data sets on sentiment analysis are discussed and the situation of balancing the data sets of the methods based on sample incrementation and sample reduction analysed to reach more reliable classification results and the positive and negative effects of these methods are revealed.
Ogul, Hamdi Atacan, Guran, Aysun
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Imbalanced Dataset Problem in Classification Algorithms

2019 1st International Informatics and Software Engineering Conference (UBMYK), 2019
Nowadays, companies continuous calculations and research with the available data to minimize the cost of personnel and time. Within the company, they provide an environment in which employees can enter their suggestions for improvement or complaints with the purpose of provide better service.
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Comparing SVM ensembles for imbalanced datasets

2010 10th International Conference on Intelligent Systems Design and Applications, 2010
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance.
Vasudha Bhatnagar, Manju Bhardwaj, Ashish Mahabal   +2 more
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Improving Logging Prediction on Imbalanced Datasets

International Journal of Open Source Software and Processes, 2016
Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the ...
Sangeeta Lal, Neetu Sardana, Ashish Sureka   +2 more
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Boosting prediction performance on imbalanced dataset

International Journal of Information and Communication Technology, 2018
Mining from imbalance data is an important problem in algorithmic and performance evaluation. When a dataset is imbalanced, the classification technique is not equal considering both the classes. It is obvious that the standard classifiers are not suitable to deal with imbalanced data, since they will likely classify all the instances into the majority
Masoumeh Zareapoor, Pourya Shamsolmoali
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A comparison for handling imbalanced datasets

2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014
In various real case, imbalanced datasets problems are inevitable, such as in metal detecting security or diagnosis of disease. With the limitations of existing learning algorithms when faced with imbalanced datasets, the prediction error is caused by the dominance of the majority against the minority class. Various techniques have been made to address
Arif Syaripudin, Masayu Leylia Khodra
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