Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit training set biases, i.e., label noise and class imbalance. While both with noisy labels class-imbalanced received tremendous attention, existing works mainly focus on one of these two biases. To fill the gap, we propose Prototypical Classifier, which does n...

An imbalanced data problem occurs in the absence of a good class distribution between classes. Imbalanced will cause classifier to be biased majority as standard classification algorithms are based on belief that training set is balanced. Therefore, it crucial find can deal with for any given task. The aim this research best method among AdaBoost, XGBoost, and Logistic Regression simulated data...

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods...

In this work, a problem associated with imbalanced text corpora is addressed. A method of converting an imbalanced text corpus into a balanced one is presented. The presented method employs a clustering algorithm for conversion. Initially to avoid curse of dimensionality, an effective representation scheme based on term class relevancy measure is adapted, which drastically reduces the dimension...

Support Vector Machines (SVMs) is a popular machine learning technique, which has proven to be very effective in solving many classical problems with balanced data sets in various application areas. However, this technique is also said to perform poorly when it is applied to the problem of learning from heavily imbalanced data sets where the majority classes significantly outnumber the minority...

The traditional biological assay is very time-consuming, and thus the ability to quickly screen large numbers of compounds against a specific biological target is appealing. To speed up the biological evaluation of compounds, high-throughput screening is widely used in the fields of biomedical, biological information, and drug discovery. The research presented in this study focuses on the use o...

Classification is one of the most fundamental tasks in the machine learning and data-mining communities. One of the most common challenges faced when trying to perform classification is the class imbalance problem. A dataset is considered imbalanced if the class of interest (positive or minority class) is relatively rare as compared to the other classes (negative or majority classes). As a resu...

In classification problem, the most important factor is training dataset which is effect accuracy rate of classification. However, we encounter with imbalanced data set in real-world applications. In this dataset, the number of images in some classes is rather less than the number of images in other classes. So estimation of classification is tent to majority class and minority classes will be ...

Many real world applications involve learning from imbalanced data sets, i.e. data where the minority class of primary importance is under-represented in comparison to majority classes. The high imbalance is an important obstacle for many traditional machine learning algorithms as they are biased towards majority classes. It is desired to improve prediction of interesting, minority class exampl...