A new hybrid filter–wrapper feature selection method for clustering based on ranking

Solorio-Fernández, Saúl; Carrasco-Ochoa, Jesús Ariel; Martínez-Trinidad, José Fco.

doi:10.1016/j.neucom.2016.07.026

Cited by 102 publications

(35 citation statements)

References 57 publications

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“…In order to improve the indexes of clustering, data sets were indexed according to such indexes as CHI and SC, it can find the ideal scaling factors. Fernandez et al [30] believed that constructing different features on the same object would affect the CHI. Therefore, based on Laplace value and CHI, a feature selection framework was proposed, which is more suitable for object construction.…”

Section: Related Workmentioning

confidence: 99%

Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

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Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three indexes. (1) Keep the number of clusters and the objects in the cluster unchanged. When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged. When changing the objects in the cluster, the three indexes will change tinily. This shows that the three indexes unable to evaluate the clustering results very well. Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes -NSC, NDBI and NCHI. Through testing on standard data sets, three new indexes can better evaluate clustering results.

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Section: Related Workmentioning

confidence: 99%

Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

View full text Add to dashboard Cite

show abstract

“…On the other hand, the purpose of the hybrid-based approaches is employing the proper performance of the wrapper model and the computational efficiency of the filter model. However, the accuracy issue may be challenging in the hybrid model since the filter and wrapper models are taken into account as two separate steps [46]. Term Variance (TV) [47], Laplacian Score for feature selection (LS) [48], Relevance-Redundancy Feature Selection (RRFS) [49], Unsupervised Feature Selection based on Ant Colony Optimization (UFSACO) [50] are some existing filter-based unsupervised feature selection methods.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method of Constrained Feature Selection by the Measurement of Pairwise Constraints Uncertainty

Berahmand

Rostami

Forouzandeh

2020

Preprint

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In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale datasets. On the other hand, data mining applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. Semi-supervised learning is a class of machine learning in which unlabeled data and labeled data are used simultaneously to improve feature selection. The goal of feature selection over partially labeled data (semi-supervised feature selection) is to choose a subset of available features with the lowest redundancy with each other and the highest relevancy to the target class, which is the same objective as the feature selection over entirely labeled data. This method actually used the classification to reduce ambiguity in the range of values. First, the similarity values of each pair are collected, and then these values are divided into intervals, and the average of each interval is determined. In the next step, for each interval, the number of pairs in this range is counted. Finally, by using the strength and similarity matrices, a new constraint feature selection ranking is proposed. The performance of the presented method was compared to the performance of the state-of-the-art, and well-known semi-supervised feature selection approaches on eight datasets. The results indicate that the proposed approach improves previous related approaches with respect to the accuracy of the constrained score. In particular, the numerical results showed that the presented approach improved the classification accuracy by about 3% and reduced the number of selected features by 1%. Consequently, it can be said that the proposed method has reduced the computational complexity of the machine learning algorithm despite increasing the classification accuracy.

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“…Filter-based subset evaluation (FBSE) was introduced simply to overcome the redundant feature issue inside filter-ranking [24]. It examines the whole subset in a multivariate way.…”

Section: Feature Selectionmentioning

confidence: 99%

A New Unified Intrusion Anomaly Detection in Identifying Unseen Web Attacks

Kamarudin

Maple

Watson

et al. 2017

Security and Communication Networks

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The global usage of more sophisticated web-based application systems is obviously growing very rapidly. Major usage includes the storing and transporting of sensitive data over the Internet. The growth has consequently opened up a serious need for more secured network and application security protection devices. Security experts normally equip their databases with a large number of signatures to help in the detection of known web-based threats. In reality, it is almost impossible to keep updating the database with the newly identified web vulnerabilities. As such, new attacks are invisible. This research presents a novel approach of Intrusion Detection System (IDS) in detecting unknown attacks on web servers using the Unified Intrusion Anomaly Detection (UIAD) approach. The unified approach consists of three components (preprocessing, statistical analysis, and classification). Initially, the process starts with the removal of irrelevant and redundant features using a novel hybrid feature selection method. Thereafter, the process continues with the application of a statistical approach to identifying traffic abnormality. We performed Relative Percentage Ratio (RPR) coupled with Euclidean Distance Analysis (EDA) and the Chebyshev Inequality Theorem (CIT) to calculate the normality score and generate a finest threshold. Finally, Logitboost (LB) is employed alongside Random Forest (RF) as a weak classifier, with the aim of minimising the final false alarm rate. The experiment has demonstrated that our approach has successfully identified unknown attacks with greater than a 95% detection rate and less than a 1% false alarm rate for both the DARPA 1999 and the ISCX 2012 datasets.

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A new hybrid filter–wrapper feature selection method for clustering based on ranking

Cited by 102 publications

References 57 publications

Analysis and improvement of evaluation indexes for clustering results

Analysis and improvement of evaluation indexes for clustering results

A Novel Method of Constrained Feature Selection by the Measurement of Pairwise Constraints Uncertainty

A New Unified Intrusion Anomaly Detection in Identifying Unseen Web Attacks

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