Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID the occurrence of situation where quantity samples belonging to one class outnumbers other by wide margin, making such models’ learning process biased towards majority class. In recent years, address this issue, several solutions have been put forward, which opt for either syntheti...

A well balanced dataset is very important for creating a good prediction model. Medical datasets are often not balanced in their class labels. Most existing classification methods tend to perform poorly on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. In thi...

Currently the best algorithms for transcription factor binding site prediction within sequences of regulatory DNA are severely limited in accuracy. In this paper, we integrate 12 original binding site prediction algorithms, and use a ‘window’ of consecutive predictions in order to contextualise the neighbouring results. We combine either random selection or Tomek links under-sampling with SMOTE...

Menurut WHO, orang yang terinfeksi virus Hepatitis C tercatat sekitar 71 juta pada 2019. Hanya 49,7% menyadari adanya penyakit C. Pencegahan dini penting dilakukan untuk meminimalisir kemungkinan buruk terjadi. Untuk memaksimalkan upaya ahli medis dalam risiko penularan, dibuat program mampu mengklasifikasikan dengan sistem deteksi otomatis menggunakan model machine learning . Random Forest dip...

Class imbalance is a condition where the amount of data in minority class smaller than that majority class. The impact dataset occurrence misclassification, so it can affect classification performance. Various approaches have been taken to deal with problem imbalances such as level approach, algorithmic and cost-sensitive learning. At level, one methods used apply sampling method. In this study...

Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Use/Land Cover has been topic interest for remote sensing community several years, but it is still fraught with technical challenges. One such challenge imbalanced nature most remotely sensed data. The asymmetric class dis...

With the development of Internet and technology, credit cards are more widely used transaction data larger. The set card fraud is a typical imbalanced problem. model should ensure that detected customer service quality guaranteed. Improving both precision recall rate focus current research. However, when constrained by level machine learning, it good choice to use different models evaluate obta...

The reasonable decision of ship detention plays a vital role in flag state control (FSC). Machine learning algorithms can be applied as aid tools for identifying detention. In this study, we propose novel interpretable decision-making model based on machine learning, termed SMOTE-XGBoost-Ship (SMO-XGB-SD), using the extreme gradient boosting (XGBoost) algorithm and synthetic minority oversampli...

Financial institutions in the form of banks provide facilities credit cards, but with development technology, fraud on card transactions is still common, so a system needed that can detect quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method Ensemble Learning which will be tested using Boosting type 3 variations, namely XGBoost, Gradient Bo...

Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies be performed using fault detection (FDC). However, class imbalance process data poses a significant obstacle to introduction FDC into equipment. Overfitting occur machine learning due diversity datasets ...