Integrating Correlation‐Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis

Wosiak, Agnieszka; Zakrzewska, Danuta

doi:10.1155/2018/2520706

Cited by 68 publications

(40 citation statements)

References 20 publications

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“…Dimensionality reduction also decreases the computational cost of algorithms. Heuristic evaluation function is used inside the Correlation-based feature selection (CFS) algorithm, which is dimensionality reeducation algorithm [45][46][47]. CFS ranks features based on their similarity with the predication class.…”

Section: Feature Selectionmentioning

confidence: 99%

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Iwendi

Khan

Anajemba

et al. 2020

Sensors

105

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The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

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Section: Feature Selectionmentioning

confidence: 99%

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Iwendi

Khan

Anajemba

et al. 2020

Sensors

105

View full text Add to dashboard Cite

show abstract

“…Where t cf is the average correlation between the features and corresponding class labels, and t f f is the average correlation between two features [17].…”

Section: Filter Feature Selection Algorithmsmentioning

confidence: 99%

Development of majority vote ensemble feature selection algorithm augmented with rank allocation to enhance Turkish text categorization

Borandağ¹,

Özçift²,

Kaygusuz³

2021

Turk J Elec Eng & Comp Sci

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The increase in the number of texts as digital documents from numerous sources such as customer reviews, news, and social media has made text categorization a crucial in order to be able to manage the enormous amount of data. The high dimensional nature of these texts requires a preliminary feature selection task to reduce the feature space with a potential increase in the prediction accuracy. In this study, we developed an ensemble feature selection method, namely majority vote rank allocation, was developed for Turkish text categorization purposes. The method uses a majority voting ensemble strategy in combination with a rank allocation approach to combine weak filters such as information gain, symmetric uncertainty, relief and correlation-based feature selection. Thus, the proposed method measures the quality of the features among all features with the majority votes of the filters and ranking allocation. The feature selection efficacy of the method was tested on two datasets, one from the literature and a newly collected dataset. The effect of the obtained features on the classification prediction performance was evaluated on top of the naive bayes, support vector machine, J48 and random forests algorithms. It was empirically observed that the developed method improved the prediction accuracies of the classifiers compared to the mentioned filters. The statistical significance of the experimental results were also validated with the use of a two-way Analysis of Variance test.

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“…This method is validated by solving a classification problem using Naïve Bays classifier, applied on microarray and text datasets. Additionally, the work in [34] successfully integrated correlation-based k-means clustering to improve the accuracy of the computer-aided diagnosis specified with cardiovascular diseases.…”

Section: Silhouette Value Literaturementioning

confidence: 99%

Unsupervised Feature Selection Using Recursive k-Means Silhouette Elimination (RkSE): A Two-Scenario Case Study for Fault Classification of High-Dimensional Sensor Data

Mallak¹,

Fathi²

2020

Preprint

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Feature selection is a crucial step to overcome the curse of dimensionality problem in data mining. This work proposes Recursive k-means Silhouette Elimination (RkSE) as a new unsupervised feature selection algorithm to reduce dimensionality in univariate and multivariate time-series datasets. Where k-means clustering is applied recursively to select the cluster representative features, following a unique application of silhouette measure for each cluster and a user-defined threshold as the feature selection or elimination criteria. The proposed method is evaluated on a hydraulic test rig, multi sensor readings in two different fashions: (1) Reduce the dimensionality in a multivariate classification problem using various classifiers of different functionalities. (2) Classification of univariate data in a sliding window scenario, where RkSE is used as a window compression method, to reduce the window dimensionality by selecting the best time points in a sliding window. Moreover, the results are validated using 10-fold cross validation technique. As well as, compared to the results when the classification is pulled directly with no feature selection applied. Additionally, a new taxonomy for k-means based feature selection methods is proposed. The experimental results and observations in the two comprehensive experiments demonstrated in this work reveal the capabilities and accuracy of the proposed method.

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Integrating Correlation‐Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis

Cited by 68 publications

References 20 publications

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Development of majority vote ensemble feature selection algorithm augmented with rank allocation to enhance Turkish text categorization

Unsupervised Feature Selection Using Recursive k-Means Silhouette Elimination (RkSE): A Two-Scenario Case Study for Fault Classification of High-Dimensional Sensor Data

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