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A decision hyper plane heuristic based artificial immune network classification algorithm

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Journal of Central South University 2013年第7期20卷 1852-1860页

作者： DENG Ze-lin TAN Guan-zheng HE Pei YE Ji-xiang School of Information Science and Engineering Central South University Changsha 410083 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410076 China

Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.

关键词： artificial immune network decision hyper plane recognition ability classification

Optimal Data Clustering by Using artificial immune network with Elitist Learning

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Optimal Data Clustering by Using Artificial Immune Network w...

An Improved artificial immune network for Multimodal Function Optimization

第26届中国控制与决策会议

作者： Li Zhonghua Fang Xianshu Zhou Jieying School of Information Science and Technology Sun Yat-sen University

ISBN: (纸本)9781479937097

Data clustering is one of the most popular techniques in data *** attributes of the objects within the same cluster should be similar to each other,while the attributes of the objects within different clusters should be *** of classical clustering algorithms suffer from such disadvantages as local optima,initial centroid selection and the lack of a way to automatically determine the optimal number of clusters,and thus fail to cluster datasets with complex *** paper proposes a novel clustering algorithm based on artificial immune network with elitist learning（Ocopt-aiNet-EL）.Some elaborated operators are implemented for automatic data clustering and the Manifold distance is used for the dissimilarity measure of antibodies.A number of benchmark cases including artificial datasets and real-world datasets are arranged to evaluate the performance of the proposed Ocopt-aiNet-EL algorithm and the other clustering algorithms,such as K-means,EAC and *** experimental results demonstrate that the proposed Ocopt-aiNet-EL is an effective and efficient clustering method.

关键词： artificial immune network data clustering elitist learning entropy measure manifold distance

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An Improved Artificial Immune Network for Multimodal Functio...

Anomaly Detection with artificial immune network

第26届中国控制与决策会议

作者： LI Zhonghua LI Jianming ZHOU Jieying School of Information Science and Technology Sun Yat-sen University

ISBN: (纸本)9781479937066

Multimodal optimization problems are typical those problems where both a global optimum and one or more local optima are included, which can be tackled with by evolutionary algorithms. This paper proposes an improved artificial immune network for multimodal function optimization. In the antibody population, antibodies are allocated to three spaces, i.e., the elitist space, the common space and the poor space. To be specific, the antibodies in the elitist space undergo self-learning mutation, those in the common space undergo elitist-learning mutation and those in the poor space undergo the random hypermutation. At each generation, the mutated antibodies with higher affinity are regrouped into a new elitist space while those with medium affinity are regrouped into a new common space. On the other hand, the old poor space is removed;instead, a group of randomly generated antibodies are grouped into a new poor space. Six benchmark functions in both 10 D and 30 D are used to evaluate the optimization performance of the proposed artificial immune network and the other existing artificial immune networks, such as opt-aiNet, IA-AIS and AINet-SL. The simulation results indicate that the proposed artificial immune network is an effective and relative efficient method in optimizing both the unimodal and multimodal functions.

关键词： artificial immune network multimodal optimization elitist learning dynamic updating

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Wuhan University Journal of Natural Sciences 2007年第5期12卷 951-954页

作者： PENG Lingxi LI Tao LIU Xiaojie CHEN Yuefeng LIU Caiming LIU Sunjun College of Computer Science Sichuan University Chengdu 610065 Sichuan China School of Information Guangdong Ocean University Zhanjiang 524025 Guangdong China

Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.

关键词： anomaly detection artificial immune network machine learning classification

artificial immune kernel clustering network for unsupervised image segmentation

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Progress in Natural Science:Materials International 2008年第4期18卷 455-461页

作者： Wenlong Huang , Licheng Jiao Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the sup- port vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coeffcient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algo- rithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predfined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

关键词： artificial immune network Kernel mapping Nonsubsampled contourlet transform Unsupervised image segmentation

NEW HYBRID AI-SVM ALGORITHM: COMBINATION OF SUPPORT VECTOR MACHINES AND artificial immune networkS

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Transactions of Nanjing University of Aeronautics and Astronautics 2006年第4期23卷 272-277页

作者：张焕萍王惠南宋晓峰南京航空航天大学自动化学院

Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SVM on large datasets, aiNet is an artificial immune system （AIS） inspired method to perform the automatic data compression, extract the relevant information and retain the topology of the original sample distribution. The output of aiNet is a set of antibodies for representing the input dataset in a simplified way. Then the SVM model is built in the compressed antibody network instead of the original input data. Experimental results show that the ai-SVM algorithm is effective to reduce the computing time and simplify the SVM model, and the accuracy is not decreased.

关键词： support vector machine artificial immune network sample reduction

A new artificial immune algorithm and its application for optimization problems

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Journal of Harbin Institute of Technology(New Series) 2006年第2期13卷 129-133页

作者：于志刚宋申民段广仁 Aerospace College Harbin Institute of Technology Harbin 150001 China

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

关键词： artificial immune network optimization algorithm preventing premature convergence.