Results 41 to 50 of about 46,171 (307)
, 2022 Comparative analysis of accuracy score of employed approaches based on original dataset features with imbalanced data.
Furqan Rustam (10196722) +5 more
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SMOTE-LOF for noise identification in imbalanced data classification
Journal of King Saud University: Computer and Information Sciences, 2022 Imbalanced data typically refers to a condition in which several data samples in a certain problem is not equally distributed, thereby leading to the underrepresentation of one or more classes in the dataset.
Asniar +2 more
doaj +1 more source
Dialog Speech Sentiment Classification for Imbalanced Datasets [PDF]
, 2021 Speech is the most common way humans express their feel- ings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen tremendous advancements in the last two decades, the task of effectively detecting under represented sen ...
Sergis Nicolaou +6 more
openaire +5 more sources
, 2022 Averaged results of models using the proposed DT-BiLTCN feature extraction technique with the imbalanced dataset.
Furqan Rustam (10196722) +5 more
core +1 more source
Classification results of machine learning models using GloVe on imbalanced dataset.
, 2022 Classification results of machine learning models using GloVe on imbalanced dataset.
Furqan Rustam (10196722) +6 more
core +1 more source
SMOTE-RUS : Combined Oversampling and Undersampling Technique to Classify the Imbalanced Autism Spectrum disorder dataset [PDF]
International Journal of Intelligent Computing and Information Sciences, 2023 The imbalanced distribution of classes is a common issue in almost classification problems. Therefore, we must be familiar with class-imbalanced techniques to handle this problem. Autism spectrum disorder(ASD) disease affects the development of the brain.
Eman ismail, Walaa Gad, Mohamed Hashem
doaj +1 more source
On the Classification of Imbalanced Datasets
International Journal of Computer Applications, 2012 In recent research the classifications of imbalanced data sets have received considerable attention. It is natural that due to the class imbalance the classifier tends to favour majority class. In this paper we investigate the performance of different methods for handling data imbalance in the microcalcification classification which is a classical ...
H. S. Sheshadri, Arun KumarM.N
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Imbalanced dataset classification using fuzzy ARTMAP and computational intelligence techniques
, 2023 Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticitystability capability and resonance property.
Kushwaha, Anita +3 more
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Anomaly Detection Model for Imbalanced Datasets
CoRR, 2020 This paper proposes a method to detect bank frauds using a mixed approach combining a stochastic intensity model with the probability of fraud observed on transactions. It is a dynamic unsupervised approach which is able to predict financial frauds. The fraud prediction probability on the financial transaction is derived as a function of the dynamic ...
Régis Houssou, Stephan Robert-Nicoud
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, 2021 The Ekush dataset has been applied in our work which is publicly available at https://shahariarrabby.github.io/ekush/. There are two proposed methods- DCGAN(Deep Convolutional Generative Adversarial Network ) and Outlier.
Moynuddin Ahmed Shibly (9985469) +1 more
core +1 more source