Extracting Useful Rules Through Improved Decision Tree Induction Using Information Entropy

Ali, Mohammed Mahmood; Qaseem, Mohammad S.; Rajamani, Lakshmi; Govardhan, A.

doi:10.5121/ijist.2013.3103

Cited by 6 publications

(3 citation statements)

References 7 publications

(16 reference statements)

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“…Hence, relevance analysis of data features is indispensable in feature selection. At present, the main methods include Chi-square check, information gain, Pearson correlation coefficient and CfsSubsetEval [13]. The limitation of Chi-square verification is the "low-frequency defect", which exaggerates the role of low-frequency features.…”

Section: Association Rules Miningmentioning

confidence: 99%

“…(13) y is the determinate value after defuzzification, Y is used to denote the fuzzy quantity in the fuzzy set Y used to denote the membership value of Y to Y → .The association rules with the largest value of y are screened out and placed in the set of association rules (MAXVALUE_r) to determine the features. The features contained in MAXVALUE_r are used as the features for malicious traffic detection.…”

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confidence: 99%

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FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Feng¹,

Kang²,

Zhang³

et al. 2020

KSII TIIS

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Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

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Section: Association Rules Miningmentioning

confidence: 99%

mentioning

confidence: 99%

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Feng¹,

Kang²,

Zhang³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Nowadays C4.5 is renamed as J48 classifier in WEKA tool, which is an open source data mining tool. The heuristic function used in this classifier is based on the concept of information entropy [39]. We used WEKA to build our classifiers.…”

Section: Feature Ideas Detailsmentioning

confidence: 99%

People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition

Necula

Strîmbei

2019

Sustainability

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The purpose of this study was to define a data science architecture for talent acquisition. The approach was to propose analytics that derive data. The originality of this paper consists in proposing an architecture to work within the process of obtaining semantically enriched data by using data science and Semantic Web technologies. We applied the proposed architecture and developed a case study-based prototype that uses analytics techniques for résumé data integrated with Linked Data technologies. We conducted a case study to identify skills by applying classification via regression, k-nearest neighbors (k-NN), random forest, naïve Bayes, support vector machine, and decision tree algorithms to résumé data that we previously described with terms from publicly available ontologies. We labeled data from résumés using terms from existing human resource ontologies. The main contribution is the extraction of skills from résumés and the mining of data that was previously described with the Semantic Web.

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Review of Literature

Rajalingam¹

2020

Text Segmentation and Recognition for Enhanced Image Spam Detection

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Extracting Useful Rules Through Improved Decision Tree Induction Using Information Entropy

Cited by 6 publications

References 7 publications

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition

Review of Literature

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