Naive Bayes and Decision Tree Classifier for Streaming Data Using HBase

Mukherjee, Aradhita; Mondal, Sudip; Chaki, Nabendu; Khatua, Sunirmal

doi:10.1007/978-981-13-3250-0_8

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The Decision Tree (DT) is a tree-like graphical representation classifier used for supervised learning in both regression and classification tasks (Géron, 2019;Mukherjee et al, 2019). It consists of nodes, with decision nodes representing attributes and leaf nodes representing class labels, that are connected via arrows, namely directed edges.…”

Section: The Decision Tree (Dt)mentioning

confidence: 99%

“…This process is repeated to determine the best-fit attribute for each node until all attributes are included in the tree. The resulting DT is then translated into rules comprising if-then statements (Mukherjee et al, 2019;Patel & Prajapati, 2018). Decision trees have several advantages, including their interpretability, simplicity, and ability to handle both categorical and numerical features.…”

Section: The Decision Tree (Dt)mentioning

confidence: 99%

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Intelligent Home: Empowering Smart Home With Machine Learning for User Action Prediction

Saleem

Hassan²,

Ali³

2023

SJUOZ

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Smart homes is an emerging technology that is transforming the way people live and interact with their homes. These homes are equipped with various devices and technologies that allow the homeowner to control, monitor, and automate various aspects of their home. This can include lighting, heating and cooling, security systems, and appliances. However, to enhance the efficiency of these homes, machine learning algorithms can be utilized to analyze the data generated from the home environment and adapt to user behaviors. This paper proposes a smart home system empowered by machine learning algorithms for enhanced user behavior prediction and automation. The proposed system is composed of three modes, including manual, automatic, and intelligent, with the objectives of maximizing security, minimizing human effort, reducing power consumption, and facilitating user interaction. The manual mode offers control and monitoring capabilities through a web-based user interface, accessible from anywhere and at any time. The automatic mode provides security alerts and appliances control to minimize human intervention. Additionally, the intelligent mode employs machine learning classification algorithms, such as decision tree, K-nearest neighbors, and multi-layer perceptron, to track and predict user actions, thereby reducing user intervention and providing additional comfort to homeowners. Experiments conducted employing the three classifiers resulted in accuracies of 97.4%, 97.22%, and 97.36%, respectively. The proposed smart home system can potentially enhance the quality of life for homeowners while reducing energy consumption and increasing security.

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Section: The Decision Tree (Dt)mentioning

confidence: 99%

Section: The Decision Tree (Dt)mentioning

confidence: 99%