Purpose: Classification of gene expression helps study disease. However, it faces two obstacles: an imbalanced class and a high dimension. The motivation this is to examine the effectiveness undersampling before feature selection on high-dimensional data with classes.Methods: Least Absolute Shrinkage Selection Operator (Lasso), which can select features, handle modeling. Random (RUS) be used de...

Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability intrusion detection systems remain questionable current research, owing to particularly imbalanced data issue. The purpose of this article is address a gap literature by illustrating benefits ensemble-based models identifying threats attacks cyber-physical power...

The class imbalance issue involves many real-world domains such as fraud detection, medical diagnosis, intrusion etc. Most classification algorithms tend to perform poorly when the training dataset is class-imbalanced. This problem gets more challenging in presence of other factors class-overlapping and noise. Among methods, undersampling a simple efficient approach which re-balances imbalanced...

The improvements in technology and computation have promoted a global adoption of Data Science. It is devoted to extracting significant knowledge from high amounts information by means the application Artificial Intelligence Machine Learning tools. Among different tasks within Science, classification probably most widespread overall. Focusing on scenario, we often face some datasets which numbe...

Many game development companies use data analysis for mining insights about users' behaviour and possible product growth. One of the most important tasks is user churn prediction. Effective prediction can help hold users in by initiating additional actions their engagement. We focused on high-value as it particular interest any business to keep paying customers satisfied engaged. consider probl...

In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources compare various machine learning algorithms binary classification problem using previously created corpus, thereby checking whether it is possible to detect extremist messages Kazakh language. To do this, authors trained models six classic machine-learning such as Suppor...

Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula precisely delineating the allowable degree of of undersampling of generalized sparse objects. The formula applies to Approximate Message Passing (AMP) algorithms for ...

We review connections between phase transitions in high-dimensional combinatorial geometry and phase transitions occurring in modern high-dimensional data analysis and signal processing. In data analysis, such transitions arise as abrupt breakdown of linear model selection, robust data fitting or compressed sensing reconstructions, when the complexity of the model or the number of outliers incr...

The problem of class imbalance along with classoverlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets which results in sub-optimal classification. Therefore, various approaches, such ...