Random forest for big data classification in the internet of things using optimal features

Lakshmanaprabu, S.K.; Shankar, K.; Ilayaraja, M.; Nasir, Abdul Wahid; Vijayakumar, V.; Chilamkurti, Naveen

doi:10.1007/s13042-018-00916-z

Cited by 120 publications

(61 citation statements)

References 28 publications

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“…The performance of the proposed FABC+CFFRideNN algorithm is analyzed and is compared with the existing methods, such as Leach+Mapreduce +Elephant Heard Optimization +Linear Kernel-Support Vector machine (Leach+Mapreduce+EHO+LK-SVM) [28], Wireless Body Area Network +Deep Convolutional Neural Network (WBAN+Deep CNN) [29], Random Forest (Artificial Bee Colony +RF) [30], and Particle Swarm Optimization+Neural network (PSO+NN).…”

Section: Comparative Methodsmentioning

confidence: 99%

“…However, it attained better accuracy, but the computational complexity was high. S. K. Lakshmanaprabu et al [30] developed a MapReduce and random forest classifier model to handle the big data in healthcare system. It effectively selects the optimal attributes to perform the data classification process.…”

Section: A Literature Surveymentioning

confidence: 99%

“… Data collection, event sifting, data combination, and deliberation associated with the data mining results a significant issue in the multi layer model [30].  To perform the data cleaning, storage, and data collection in the real time analytics model faces several challenges in the IoT technology.…”

Section: B Challengesmentioning

confidence: 99%

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Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

Yousir*¹

2019

IJEAT

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The typical Internet of Things (IoT) device gathers a huge amount of data specifically termed as big data framework, which transfers the collected data from the sensing layer to the information processing layer. Various big data classification methods are adopted in the industrial applications, and smart cities, but accurately classifying the data in the IoT network poses a challenging task in the research community. Therefore, an effective big data classification model using spark-based architecture is proposed in this research. The big data classification is performed at the master node using the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm (FABC-CFFRideNN). The concept of fictional computing is adopted by the rider optimization algorithm (ROA) to update the position of rider groups based on success rate and the foraging behavior of fruit flies along with the rider parameters is used to enhance to performance of data classification using the proposed CFFRideNN classifier. Moreover, the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm attained better performance using the metrics, namely accuracy, specificity, and sensitivity with the values of 95.382%, 95.81%, and 98.824% for training percentage without node velocity.

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Section: Comparative Methodsmentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

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Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

Yousir*¹

2019

IJEAT

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“…Random Forest classifier was used in IOT based health care system to classify the E-health data with optimal features [10]. Better classification accuracy was acquired and compared with Gaussian mixture model and logistic regression.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Models for Relevant Feature Identification and Classification of Thyroid Data

Nandhinidevi*,

Poorani,

Brindha

2020

IJITEE

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Inappropriate creation of thyroid glands turn into prime subject of concern amongst Indian women. The two key chaos of thyroid which ought to be taken care at the earliest are hypothyroidism and hyperthyroidism. The improper secretion of thyroid may leads to obesity, fertility related problems, feeling depressed, etc,. Most of the thyroid problems can be managed if it has been properly treated. In recent years a number of models have been developed to investigate the thyroid-disorder. The laboratory tests are conducted to find the levels of the hormones and some of the physical examinations are used to identify the presences of thyroid. These examinations and test results are taken as the feature for developing the model. The feature importance can promote the performance of ML algorithms. The core intention of this study is to improve the classification performance by identifying the relevant features before classification. In this work, random-forest model is considered for identifying the important features and KNN algorithm is implemented for multiclass-classification to envisage the kind of thyroid chaos. Applying KNN after the feature selection improves the prediction accuracy. The developed model can be used to predict the presence of thyroid so that it can be treated accordingly.

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“…Due to the Health field, the Big Data knowledge could contribute with the plan to help with the end goal of investigation and management of the huge measures of health data. The contribution by Chilamkurti et al "Random forest for big data classification in the internet of things using optimal features" implemented the e-health, big data in IoT utilizing innovative classifier and map reduce process [2]. A supervised, Random Forest Classifier(RFC) for grouping of e health data, this data are collected from by sensor devices and actuators, which will wear patients who suffer from different ailments, As medical data is with multiple attributes, medical data mining differs from other one.…”

mentioning

confidence: 99%

Special issue on Machine learning approaches and challenges of missing data in the era of big data

Jeon

Sangaiah

Chen

et al. 2019

Int. J. Mach. Learn. & Cyber.

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Random forest for big data classification in the internet of things using optimal features

Cited by 120 publications

References 28 publications

Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

Machine Learning Models for Relevant Feature Identification and Classification of Thyroid Data

Special issue on Machine learning approaches and challenges of missing data in the era of big data

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