of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques

<p>Breast cancer is one of the significant deaths causing diseases women around globe. Therefore, high accuracy in prediction models vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm predict stage breast using unbalanced surveillance epidemiology end result ...

Perkembangan teknologi yang pesat melahirkan metode pembayaran elektronik, salah satunya adalah kartu kredit. Penggunaan kredit dinilai memudahkan pemilik dalam bertransaksi. Kemudahan itu sering kali disalahgunakan oleh orang-orang tidak bijak, yaitu dengan cara belanja kompulsif. Perilaku kompulsif menggunakan dapat berdampak terhadap risiko gagal bayar atau default. default melakukan hutang ...

The Fundamental Review of the Trading Book is a market risk measurement and management regulation recently issued by Basel Committee. This reform, often referred to as “Basel IV”, intends strengthen financial system. newest capital standard relies on use Expected Shortfall. measure requires get sufficient information in tails ensure its reliability, this one has be alimented quantity relevant d...

Abstract To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction the whole (whole-lung analysis) imaging- dosimetric-based segmentation (multi-region performed. Patients RP or...

The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to different approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with the kernel classifiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean sp...

In the construction industry, evaluating the financial status of a contractor is a challenging task due to the myriad of the input data as well as the complexity of the working environment. This article presents a novel hybrid intelligent approach named as Evolutionary Least Squares Support Vector Machine Inference Model for Predicting Contractor Default Status (ELSIM-PCDS). The proposed ELSIM-...

The Synthetic Minority Over Sampling TEchnique (SMOTE) is a widely used technique to balance imbalanced data. In this paper we focus on improving SMOTE in the presence of class noise. Many improvements of SMOTE have been proposed, mostly cleaning or improving the data after applying SMOTE. Our approach differs from these approaches by the fact that it cleans the data before applying SMOTE, such...