Extracting rules for classification problems: AIS based approach

Kahramanlı, Humar; Allahverdi, Novruz

doi:10.1016/j.eswa.2009.01.029

Cited by 41 publications

(17 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The generalization to multiple knowledge sets is straight forward and is not discussed here. We will discuss incorporation of these knowledge sets into QPP (9) and remark that their incorporation into QPP (10) will be on similar lines. Here, the idea is to use the fact that in each QPP of linear TWSVM, the objective function corresponds to a particular class and the constraints are determined by patterns of the other class.…”

Section: Knowledge Based Twin Svmmentioning

confidence: 99%

Knowledge based Least Squares Twin support vector machines

Kumar

Khemchandani²,

Gopal

et al. 2010

Information Sciences

View full text Add to dashboard Cite

Section: Knowledge Based Twin Svmmentioning

confidence: 99%

Knowledge based Least Squares Twin support vector machines

Kumar

Khemchandani²,

Gopal

et al. 2010

Information Sciences

View full text Add to dashboard Cite

“…Artificial Immune Systems (AIS) are defined as intelligent computational systems inspired by Human Immune System (HIS), which are applied to anomaly detection [3,[21][22][23], optimization [24,25], clustering and classification [26][27][28][29][30], and so on. The most widely used theories in AIS are self/nonself theory and clonal selection theory.…”

Section: Introductionmentioning

confidence: 99%

Run-time malware detection based on positive selection

Zhang

2011

J Comput Virol

View full text Add to dashboard Cite

This paper presents a supervised methodology that detects malware based on positive selection. Malware detection is a challenging problem due to the rapid growth of the number of malware and increasing complexity. Run-time monitoring of program execution behavior is widely used to discriminate between benign and malicious executables due to its effectiveness and robustness. This paper proposes a novel classification algorithm based on the idea of positive selection, which is one of the important algorithms in Artificial Immune Systems (AIS), inspired by positive selection of T-cells. The proposed algorithm is applied to learn and classify program behavior based on I/O Request Packets (IRP). In our experiments, the proposed algorithm outperforms ANSC, Naïve Bayes, Bayesian Networks, Support Vector Machine, and C4.5 Decision Tree. This algorithm can also be used in general purpose classification problems not just two-class but multi-class problems.

show abstract

“…Then several works appeared in literature [3,[7][8][9][10][11][12][13][14][15][16][17] to extract rules from NN in different ways. Most recently, Naveen et al, [18] extracted rules from differential evolution trained radial basis function network (DERBF) using GATree.…”

Section: Introductionmentioning

confidence: 99%

Data Mining via Rules Extracted from GMDH: An Application to Predict Churn in Bank Credit Cards

Naveen

Ravi

Rao

2010

Knowledge-Based and Intelligent Information and Engineering Systems

View full text Add to dashboard Cite

Abstract. This paper proposes a hybrid method to extract rules from the trained Group Method of Data Handling (GMDH) neural network using Decision Tree (DT). The outputs predicted by the GMDH for the training set along with the input variables are fed to the DT for extracting the rules. The effectiveness of the proposed hybrid is evaluated on four benchmark datasets namely Iris, Wine, US Congressional, New Thyroid and one small scale data mining dataset churn prediction using 10-fold cross-validation. One important conclusion from the study is that we obtained statistically significant accuracies at 1% level in the case of churn prediction and IRIS datasets. Further, in the present study, we noticed that the rule base size of proposed hybrid is less in churn prediction and IRIS datasets when compared to that of the DT and equal in the case of remaining datasets.

show abstract

Extracting rules for classification problems: AIS based approach

Cited by 41 publications

References 28 publications

Knowledge based Least Squares Twin support vector machines

Knowledge based Least Squares Twin support vector machines

Run-time malware detection based on positive selection

Data Mining via Rules Extracted from GMDH: An Application to Predict Churn in Bank Credit Cards

Contact Info

Product

Resources

About