Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

rez-Díaz,; #, D. Ruano-Ord; Fdez-Riverola, Florentino; ndez,

doi:10.1155/2016/5945192

Cited by 11 publications

(5 citation statements)

References 38 publications

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“…5 and Fig. 9 examine the outcomes of classification analysis for MOMBD-CDD method upon the applied ECUE spam dataset [26][27][28][29]. The table values correspond that the HELF technique resulted in minimum accuracy of 75%.…”

Section: Dataset Usedmentioning

confidence: 90%

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

Mansour¹,

Al-Otaibi²,

Al-Rasheed³

et al. 2021

Computers, Materials &Amp; Continua

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Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of massive volumes of data by different applications. It is challenging to apply existing data mining tools and techniques directly in these big data streams. At the same time, streaming data from several applications results in two major problems such as class imbalance and concept drift. The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection (MOMBD-CDD) method on High-Dimensional Streaming Data. The presented MOMBD-CDD model has different operational stages such as pre-processing, CDD, and classification. MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique (SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE, Glowworm Swarm Optimization (GSO) algorithm is employed. Besides, Statistical Test of Equal Proportions (STEPD), a CDD technique is also utilized. Finally, Bidirectional Long Short-Term Memory (Bi-LSTM) model is applied for classification. In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model, GSO-based hyperparameter tuning process is carried out. The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection (NSL KDDCup) dataset and ECUE spam dataset. An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model. The proposed model attained high accuracy of 97.45% and 94.23% on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.

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Section: Dataset Usedmentioning

confidence: 90%

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

Mansour¹,

Al-Otaibi²,

Al-Rasheed³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Table 5 and Fig. 14 computes a detailed comparative results analysis of the CIDD-ADODNN model on the test Spam dataset [23][24][25]. The resultant scores reported that HELF and KNN models have depicted inferior performance by obtaining lower accuracy values of 0.750 and 0.818, respectively.…”

Section: Performance Validationmentioning

confidence: 99%

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

Priya

Uthra

2021

Complex Intell. Syst.

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In present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

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“…The text mining research field (Tandel, Jamadar, & Dudugu, 2019) emerged as the prevalent form of exploiting token information to solve problems such as text classification, information retrieval, etc. Furthermore, the first well-known ML proposal for spam filtering 4 introduced by Paul Graham, and many others introduced later (Méndez, Glez-Peña, Fdez-Riverola, Díaz, & Corchado, 2009;Pérez-Díaz, Ruano-Ordás, Fdez-Riverola, & Méndez, 2016), take advantage of token-based information. In this manner, topic models (Grün & Hornik, 2011) emerged with the aim of studying terms that are usually found together in texts and therefore allow for topic detection without using semantic information about the connections between terms.…”

Section: Related Workmentioning

confidence: 99%

SDRS: A new lossless dimensionality reduction for text corpora

Mendizabal

Basto‐Fernandes

Ezpeleta

et al. 2020

Information Processing & Management

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Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

Cited by 11 publications

References 38 publications

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

SDRS: A new lossless dimensionality reduction for text corpora

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