Random forest age estimation model based on length of left hand bone for Asian population

Darmawan, Mohd Faaizie; Abidin, Ahmad Firdaus Zainal; Kasim, Shahreen; Sutikno, Tole; Budiarto, Rahmat

doi:10.11591/ijece.v10i1.pp549-558

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…In the bioinformatics, Pang et al [ 9 ] propose a method to mitigate the computational complexity of RNA simulation software by a typical random forest. Darmawan et al [ 10 ] propose an age estimation model in the bioinformatics field. In the economics field, Park et al [ 11 ] propose two stages of short-term load forecasting by random forest and deep neural networks to reduce energy costs.…”

Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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“…As well as enabling the implementation of numerous machine learning strategies, such as grouping and regression; classification; rule extraction; and dimensional reduction. It also enables the rapid and simple creation of machine learning approaches for large-scale applications [67], [111]- [116].…”

Section: Apache Spark Mllib 20mentioning

confidence: 99%

Large scale data analysis using MLlib

et al. 2021

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Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.

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“…Various traditional clustering algorithms have been applied to predict heart diseases [2]- [12]. All of them have their pros and cons.…”

Section: Introductionmentioning

confidence: 99%

A novel salp swarm clustering algorithm for prediction of the heart diseases

Sureja¹,

Chawda

Vasant³

2022

IJEECS

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Heart <span>diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical </span>practitioners.

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Random forest age estimation model based on length of left hand bone for Asian population

Cited by 4 publications

References 50 publications

A New Random Forest Algorithm Based on Learning Automata

A New Random Forest Algorithm Based on Learning Automata

Large scale data analysis using MLlib

A novel salp swarm clustering algorithm for prediction of the heart diseases

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