A missing power data filling method based on improved random forest algorithm

Deng, Wei; Guo, Yixiu; Liu, Jie; Li, Yong; Liu, Dingguo; Zhu, Liang

doi:10.23919/cjee.2019.000025

Cited by 73 publications

(41 citation statements)

References 7 publications

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“…2 Principle of filling random forest. The bootstrap resampling technique is firstly used where multiple samples are randomly selected from the original training dataset x to generate a new training dataset [ 32 ]. Then, multiple decision trees are built to form the random forest which then finally averages the output of each decision tree to determine the final filling result y [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…Unlike the bagging learning algorithm, where the models are made independently, gradient boosting makes its models sequentially by iteration to minimize the error of models learned earlier [34]. The gradient boosting algorithm learns a predictive model by combining M additive tree models (T 0 , T 1 , …, T n ) to predict the results as shown in the equation below: [32]. Then, multiple decision trees are built to form the random forest which then finally averages the output of each decision tree to determine the final filling result y [33].…”

Section: Gradient Boosting Algorithmmentioning

confidence: 99%

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Predictive Supervised Machine Learning Models for Diabetes Mellitus

2020

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Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively. Keywords Machine learning • Predictive model • Diabetes mellitus • Diabetes mellitus type 2 • Random forest This article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar".

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Section: Methodsmentioning

confidence: 99%

Section: Gradient Boosting Algorithmmentioning

confidence: 99%

Predictive Supervised Machine Learning Models for Diabetes Mellitus

2020

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“…2) Random forest: Decision Trees present specific difficulties when generating the model, since creating a tree with many leaves can cause an over-fitting that may not be the most appropriate decision. Random trees are, therefore, used to achieve greater assertiveness [18]. Random trees use several trees averaging the final prediction of each tree.…”

Section: ) Decision Treementioning

confidence: 99%

Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database

Huamaní¹,

Mantari²,

Román-Gonzalez³

2020

IJACSA

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Terrorist attacks affect the confidence and security of citizens; it is a violent form of a political struggle that ends in the destruction of order. In the current decade, along with the growth of social networks, terrorist attacks around the world are still ongoing and have had potential growth in recent years. Consequently, it is necessary to identify where the attacks were committed and where is the possible area for an attack. The objective is to provide assertive solutions to these events. As a solution, this research focuses on one of the branches of artificial intelligence (AI), which is the Automatic Learning, also called Machine Learning. The idea is to use AI techniques to visualize and predict possible terrorist attacks using classification models, the decision trees, and the Random Forest. The input would be a database that has a systematic record of worldwide terrorist attacks from 1970 to the last recorded year, which is 2018. As a final result, it is necessary to know the number of terrorist attacks in the world, the most frequent types of attacks and the number of seizures caused by region; furthermore, to be able to predict what kind of terrorist attack will occur and in which areas of the world. Finally, this research aims to help the scientific community use artificial intelligence to provide various types of solutions related to global events.

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“…The performance of the space-based model is highly dependent on the correlation between inputs and outputs. With the development of artificial intelligence (AI) technology, hundreds of space-based data imputation models have been established using AI-based methods, such as k-nearest neighbour (kNN) [15], random forest (RF) [16], cumulative linear regression (CLR) [17], and extreme learning machine (ELM) [18,19]. Compared with the time-based model, the space-based model ignores the correlation between the measured values at different times.…”

Section: Literature Reviewmentioning

confidence: 99%