Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends
Abstract
:1. Introduction
 Supervised learning. This learning algorithm uses samples of input vectors as their target vectors. The target vectors are typically referred to as labels. Supervised learning algorithm’s goal is to estimate the output vector for a specific input vector using learning algorithms. Usercases that have target identifiers are contained in a finite distinct group. This is typically referred to as classification assignment. When these targeted identifiers consists of one or more constant variables, they are called regression assignment [5].
 Unsupervised learning. This learning algorithm does not require labeling of the training set. The objective of this type of learning is to identify hidden patterns of the analogous samples in the input data. This is commonly called clustering. This learning algorithm provides suitable internal understanding of the inputsource information, by preprocessing the baseline inputsource, making it possible to reposition it into a different variable space of the algorithm. The preprocessing phase enhances the outcome of a successive ML algorithm. This is typically referred to as a feature extraction [7].
 Reinforcement learning. This learning algorithm involves deploying similar actions or series of actions when confronted with same problem with the aim of maximizing payoff [8]. Any outcome that does not lead to favorable expectation is dropped and conversely. Expectedly, this type of algorithm consumes lots of memory space and is predisposed in applications that are executed continuously.
2. Taxonomies of Supervised and Unsupervised ML Algorithms
2.1. Supervised ML Algorithm
2.1.1. Classification Tasks
KNearest Neighbors (KNN)
Naive Bayes
2.1.2. Regression Tasks
Linear Regression
2.1.3. Combining Classification and Regression Tasks
Support Vector Machine (SVM)
Classification and Regression Trees (CART)
Random Forests
Bootstrap Aggregating
2.2. Unsupervised ML Algorithm
2.2.1. Clustering
KMeans
DensityBased Spatial Clustering of Applications with Noise (DBSCAN)
2.2.2. Feature Extraction
Principal Component Analysis (PCA)
Canonical Correlation Analysis (CCA)
2.3. Neural Networks
3. Research Trends and Open Issues
3.1. Privacy and Security
3.2. RealTime Data Analytics
4. Conclusions and Recommendations
Author Contributions
Funding
Conflicts of Interest
References
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Data Analysis Tasks  ML Algorithm  Advantages  Disadvantages 

Classification  KNN 


Naive Bayes 

 
Regression  Linear Regression 


Combining Classification and Regression  SVM 


Random Forest 

 
Bootstrap Aggregating 


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Alsharif, M.H.; Kelechi, A.H.; Yahya, K.; Chaudhry, S.A. Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends. Symmetry 2020, 12, 88. https://doi.org/10.3390/sym12010088
Alsharif MH, Kelechi AH, Yahya K, Chaudhry SA. Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends. Symmetry. 2020; 12(1):88. https://doi.org/10.3390/sym12010088
Chicago/Turabian StyleAlsharif, Mohammed H., Anabi Hilary Kelechi, Khalid Yahya, and Shehzad Ashraf Chaudhry. 2020. "Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends" Symmetry 12, no. 1: 88. https://doi.org/10.3390/sym12010088