Design of a Machine Learning Based Predictive Analytics System for Spam Problem

Yüksel, Asım Sinan; Cankaya, Sadi Fuat; Üncü, İsmail Serkan

doi:10.12693/aphyspola.132.500

Cited by 20 publications

(6 citation statements)

References 14 publications

(12 reference statements)

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“…SVM: SVM algorithm is a supervised learning algorithm and binary classifier [ 25 ]. It is mostly used to solve classification problems [ 26 ]. Support vector machine (SVM) is used to separate data belonging to two classes in a most suitable way, and to implement this procedure, hyperplanes are specified [ 27 ].…”

Section: Classificationmentioning

confidence: 99%

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Kırelli

Arslankaya

2020

Computational Intelligence and Neuroscience

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As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.

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Section: Classificationmentioning

confidence: 99%

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Kırelli

Arslankaya

2020

Computational Intelligence and Neuroscience

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“…The DT that predicts the target variable through di erent input variables is a widely used classi er among machine learning techniques [35]. There are three types of nodes in a tree, namely the root, nonterminal, and leaf.…”

Section: Decision Tree (Dt) Learning Techniquementioning

confidence: 99%

Integration of Machine Learning Techniques and Control Charts for Multivariate Processes

2019

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Using multivariate control chart instead of univariate control chart for all variables in processes provides more time and labor advantages that are of signi cance in the relations among variables. However, the statistical calculation of the measured values for all variables is regarded as a single value in the control chart. Therefore, it is necessary to determine which variable(s) are the cause of the out-of-control signal. E ective corrective measures can only be developed when the causes of the fault(s) are correctly determined. The present study was aimed at determining the machine learning techniques that could accurately estimate the fault types. Through the Hotelling T 2 chart, out-ofcontrol signals were identi ed and the types of faults a ected by the variables were speci ed. Various machine learning techniques were used to compare classi cation performances. The developed model was employed in the evaluation of paint quality in a painting process. Arti cial Neural Networks (ANNs) was determined as the most successful technique in terms of the performance criteria. The novelty of this study lies in its classi cation of the faults according to their types instead of those of the variables. De ning the faults based on their types facilitates taking e ective and corrective measures when needed.

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“…The hallmarks of this classification reasoning are the support vectors chosen from the training set, and they are located on the closest points of both classes [13]. SVM has a good generalization performance and they work well in practice [14,15]. More information about SVM can be obtained from [12,13].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Comparison of Different Training Data Reduction Approaches for Fast Support Vector Machines Based on Principal Component Analysis and Distance Based Measurements

Güraksın¹,

Uğuz²

2018

International Journal of Computational and Experimental Science and Engineering

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Support vector machine is a supervised learning algorithm, which is recommended for classification and nonlinear function approaches. Support vector machines require remarkable amount of memory with time consuming process for large data sets in the training process. The main reason for this is the solving a constrained quadratic programming problem within the algorithm. In this paper, we proposed three approaches for identifying the non-critical points in training set and remove these noncritical points from the original training set for speeding up the training process of support vector machine. For this purpose, we used principal component analysis, Mahalanobis distance and Euclidean distance based measurements for the elimination of non-critical training instances in the training set. We compared the proposed methods in terms of computational time and classification accuracy between each other and conventional support vector machine. Our experimental results show that principal component analysis and Mahalanobis distance based proposed methods have positive effects on computational time without degrading the classification results than the Euclidean distance based proposed method and conventional support vector machine.

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Design of a Machine Learning Based Predictive Analytics System for Spam Problem

Cited by 20 publications

References 14 publications

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Integration of Machine Learning Techniques and Control Charts for Multivariate Processes

Comparison of Different Training Data Reduction Approaches for Fast Support Vector Machines Based on Principal Component Analysis and Distance Based Measurements

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