Effect of feature selection methods on machine learning classifiers for detecting email spams

Trivedi, Shrawan Kumar; Dey, Subhrakanti

doi:10.1145/2513228.2513313

Cited by 33 publications

(36 citation statements)

References 10 publications

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“…Sabiendo que el rendimiento de los clasificadores SVM se ve afectado por el kernel utilizado [71], se consideraron varias funciones núcleo, para conocer su influencia en un sistema de reconocimiento de hablantes. Se utilizaron los siguientes kernels, implementados en el paquete de algoritmos de minería de datos WEKA: kernel polinómico normalizado (NPK), kernel polinómico (PK), kernel radial (RBF), y kernel universal basado en la función Pearson VII (puk) [70].…”

Section: Resultsunclassified

Data Mining applied to Forensic Speaker Identification

Univaso

Ale

Gurlekian

2015

IEEE Latin Am. Trans.

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In this paper we analyze the advantages of using data mining techniques and tools for data fusion in forensic speaker recognition. Segmental and suprasegmental features were employed in 28 different classifiers, in order to compare their performances. The selected classifiers have different learning techniques: lazy or instance-based, eager and ensemble. Two approaches were employed on the classification task: the use of all features and the use of a feature subset, selected with a gain ratio methodology. The best performances, with all features, were obtained by three classifiers: Logistic Model Tree (eager), LogitBoost (ensemble) and Multilayer Perceptron (eager). Support Vector Machine (eager) proved to be a good classifier if a Pearson VII function-based universal kernel was used. When low dimensional features were selected, ensemble classifiers exceeded the performance of all others classifiers. Segmental and tone features demonstrated the best speaker discrimination capabilities, followed by duration and quality voice features. Evaluation was performed on Argentine-Spanish voice samples from the Speech_Dat database recorded on a fixed telephone environment. Different recording sessions and channels for the test segments were added and the Z-norm procedure was applied for channel compensation.

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Section: Resultsunclassified

Data Mining applied to Forensic Speaker Identification

Univaso

Ale

Gurlekian

2015

IEEE Latin Am. Trans.

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“…Support vector machine has become popular owing to its significantly better empirical performance compared with other techniques (Trivedi & Dey, 2013). SVM, with a strong mathematical basis, is closely related to some well-established theories in statistics and is capable of nonlinear separation using the hyperplane idea.…”

Section: Methodsmentioning

confidence: 99%

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Savareh

Bashiri

Behmanesh

et al. 2018

PeerJ

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IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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“…Ouyang et al [41] conducted a large scale empirical study regarding the effectiveness of using packet and flow features to detect email spam at an organization, based on decision tree and Rulefit. Several other related works can be found in [11,25,19,52,58,59,67,70,66].…”

Section: Related Workmentioning

confidence: 99%