COMPARISON OF DT&amp; GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES

Alsaleem, Maan Y. Anad; Hasoon, Safwan O.

doi:10.21303/2461-4262.2020.001132

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…A. DT J48, is one of algorithms used for making a decision tree developed by Ross Quinlan [1,8]. The tree is built in the same way as building Iterative Dichotomiser 3 (ID3), where the contract is chosen based on the concept of gain, where the attribute with the highest classification ability (highest gain) is considered as the root of the tree that is branched into leaves.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Bank Loan Risks Using Machine Learning Algorithms

Alsaleem¹,

Hasoon²

2020

AL-Rafidain Journal of Computer Sciences and Mathematics

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Bank loans play a crucial role in the development of banks investment business. Nowadays, there are many risk-related issues associated with bank loans. With the advent of computerization systems, banks have become able to register borrowers' data according their criteria. In fact, there is a tremendous amount of borrowers' data, which makes the process of load management a challenging task. Many studies have utilized data mining algorithms for the purpose of loans classification in terms of repayment or when the loans are not based on customers' financial history. This kind of algorithms can help banks in making grant decisions for their customers. In this paper, the performance of machine learning algorithms has been compared for the purpose of classifying bank loan risks using the standard criteria and then choosing (Multilayer Perceptron) as it has given best accuracy compared to RandomForest, BayesNet, NaiveBayes and DTJ48 algorithms.

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Section: Methodsmentioning

confidence: 99%

“…The dataset used for classification purpose entitled "German Credit data" collected from UCI repository that contains 1000 Instances, 11 attributes as shown in Table (1).…”

Section: Datasetmentioning

confidence: 99%

Predicting Bank Loan Risks Using Machine Learning Algorithms

Alsaleem¹,

Hasoon²

2020

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…This approach enhances the gradient boosting-based calculation method for the objective function and saves computation time. Parallel computing is automatically achieved throughout the training phase to address large data science issues rapidly and precisely [20]. XGBoost main premise is to learn new features by including a tree structure, fitting the residuals of the final prediction, and calculating the sample score.…”

Section: Xgboostmentioning

confidence: 99%

Network Traffic Prediction Based on Time Series Modeling

AlSaleem

2023

Iraqi Journal of Science

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Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and (MAPE). The results showed the possibility of modeling the network traffic time series and that the performance of the linear regression model is the best compared to the rest of the models for both series.

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“…The purpose of decision tree learning is to produce a decision tree with strong generalization ability and a strong ability to deal with unseen strength [39]. Often, a decision tree is built based on a data set [40].…”

Section: Decision Treementioning

confidence: 99%

Development of heart attack prediction model based on ensemble learning

Hasan

Saleh

2021

EEJET

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With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem. Heart attack is one of the most common diseases and one of the leading causes of death, so finding a system that can accurately and reliably predict early diagnosis is an essential and influential step in treating such diseases. Researchers have used various data mining and machine learning techniques to analyze medical data, helping professionals predict heart disease. This paper presents various features related to heart disease, and the model is based on ensemble learning. The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. Furthermore, using machine learning algorithms (Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting) for prediction on the Framingham Heart Study dataset and compared with the proposed methodology. The results show that the feasibility and effectiveness of the proposed prediction method based on group learning provide accuracy for medical recommendations and better accuracy than the single traditional machine learning algorithm.

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COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES

Cited by 4 publications

References 10 publications

Predicting Bank Loan Risks Using Machine Learning Algorithms

Predicting Bank Loan Risks Using Machine Learning Algorithms

Network Traffic Prediction Based on Time Series Modeling

Development of heart attack prediction model based on ensemble learning

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