2019-10

2018 International Conference on Advanced Science and Engineering (ICOASE)

Currently, the main problem in DNA Microarray is classification due to the thousands of numbers of genes together, and this huge number of genes can make the classification task very difficult. Therefore, feature selection is a very important task for gene classification. This paper presents a new model which uses a Cuttlefish Algorithm (CFA) to select the most informative features, while K-Nearest Neighbor (KNN) is used to measure the quality of the selected features that are produced by the CFA. Eight datasets are used to evaluate the performance of the proposed model and compared with the performance of four well-known existing classification techniques such as KNN, DT, Hidden Markov models (HMM), and SVM. The obtained results show that the proposed technique outperforms these existing techniques in five datasets among eight datasets.

2015-07

ICAINN 2015 : 17th International Conference on Artificial Intelligence and Neural Networks

Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of a distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas the decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

2014-09

International Conference on Computer Science (ICCS 2014)

This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. The proposed algorithm considers mainly two processes: reflection and visibility. Reflection process simulates light reflection mechanism used by these layers, while visibility process simulates the visibility of matching patterns of the cuttlefish. To show the effectiveness of the algorithm, it is tested with some other popular bio-inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior when compared with these algorithms.

2014-05

3rd International Conference on System Engineering and Modeling (ICSEM)

This paper presents a new combinational approach to select an optimal subset of features of Intrusion Detection Data. The main idea is to take advantage of ID3 Algorithm and Bees Algorithm (BA). In the proposed feature selection model, BA is used as a search strategy for a subset generation and ID3 is used as the classifier. The effectiveness of the model is tested on the random subsets collected from KDD-cup 99 data set. Standard Intrusion Detection System (IDS) measurements such as detection rate, false alarm rate, and classification accuracy are used to evaluate the performance of the new model. The results obtained show that the feature subset generated by the proposed ID3-BA can achieve classification accuracy and detection rate superior to the results obtained by using all features.