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Using genetic algorithms to optimize artificial neural networks

Journal of Convergence information technology 2010年第8期5卷 6-6页

作者： Ding, Shifei Xu, Li Su, Chunyang Zhu, Hong School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Artificial Neural Networks (ANNs), as a nonlinear and adaptive information processing systems, play an important role in machine learning, artificial intelligence, and data mining. But the performance of ANNs is sensitive to the number of neurons, and chieving a better network performance and simplifying the network topology are two competing objectives. While Genetic Algorithms (GAs) is a kind of random search algorithm which simulates the nature selection and evolution, which has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. This paper makes a brief survey on ANNs optimization with GAs. Firstly, the basic principles of ANNs and GAs are introduced, by analyzing the advantages and disadvantages of GAs and ANNs, the superiority of using GAs to optimize ANNs is expressed. Secondly, we make a brief survey on the basic theories and algorithms of optimizing the network weights, optimizing the network architecture and optimizing the learning rules, and make a discussion on the latest research progresses. At last, we make a prospect on the development trend of the theory.

关键词： Genetic algorithms

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An improved bp neural network algorithm based on factor analysis

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Journal of Convergence information technology 2010年第4期5卷

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Liu, Xiaoliang Chen, Jinrong School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has the ability of large scale computing and has unique advantages when dealing with nonlinear high dimensional data. But when we manipulate high dimensional data with BP neural network, many feature variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the the accuracy of recognition finally. Factor analysis (FA) is a multivariate analysis method which transforms many feature variables into few synthetic variables. Aiming at the characteristics that the samples processed have more feature variables, combining with the structure feature of BP neural network, a FA-BP neural network algorithm is proposed. Firstly we reduce the dimensionality of the feature factor using FA, and then regard the features reduced as the input of the BP neural network, carry on network training and simulation with low dimensional data that we get. This algorithm here can simplify the network structure, improve the velocity of convergence, and save the running time. Then we apply the new algorithm in the field of pest prediction to emulate. The results show that under the prediction precision is not reduced, the error of the prediction value is reduced by using the new algorithm, and therefore the algorithm is effective.

关键词： Neural networks

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A novel ant colony genetic hybrid algorithm

Journal of Software

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Journal of Software 2010年第11期5卷 1179-1186页

作者： Gao, Shang Zhang, Zaiyue Cao, Cungen School of Computer Science and Technology Jiangsu University of Science and Technology Zhenjiang 212003 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

By use of the properties of ant colony algorithm and genetic algorithm, a novel ant colony genetic hybrid algorithm, whose framework of hybrid algorithm is genetic algorithm, is proposed to solve the traveling salesman problems. The selection operator is an artificial version of natural selection, and chromosomes with better length of tour have higher probabilities of being selected in the next generation. Based on the properties of pheromone in ant colony algorithm the ant colony crossover operation is given. Four mutation strategies are put forward using the characteristic of traveling salesman problems. The hybrid algorithm with 2-opt local search can effectively find better minimum beyond premature convergence. Ants choose several tours based on trail, and these tours will replace the worse solution. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, all the 4 hybrid algorithms are proved effective. Especially the hybrid algorithm with strategy D is a simple and effective better algorithm than others. © 2010 ACADEMY PUBLISHER.

关键词： Genetic algorithms

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Efficient SLCA computing Service for XML Filtering in IaaS

Efficient SLCA Computing Service for XML Filtering in IaaS

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International Asia-Pacific Web Conference (APWEB)

作者： Chenjing Zhang Xiaoling Wang Aoying Zhou Shanghai Key Laboratory of Intelligent Information Fudan University China College of Information Technology Shanghai Ocean University China Shanghai Key Laboratory of Trustworthy Computing East China Jiao Tong University China

Cloud provides dynamically computing services for large scales of data over the Internet. IaaS(information as a service) is one of utilities to provide information service in Cloud computing. Large scales of XML data are produced continually in Internet. Efficient information filtering services are needed. Previous XML filter approaches aim at XPath queries. However, many users tend to use keywords to describe requirements. SLCA (Smallest Lowest Common Ancestor)-based XML keyword search is one of the most important information retrieval approaches. Former approaches focus on building centralized index for a large scale of XML document collection and can't process continuous XML streams. This paper addresses SLCA computing service for continuous XML document. A novel SLCA computing service is designed, where SLCAs are obtained in one scan of XML stream. We demonstrate the efficiency of our algorithms analytically and experimentally.

关键词： XML information filtering Large-scale systems information filters Cloud computing Web and internet services keyword search information retrieval Buildings Algorithm design and analysis

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Voting conditional random fields for multi-label image classification

Voting conditional random fields for multi-label image class...

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Congress on Image and Signal Processing, CISP

作者： Xishun Wang Xi Liu Zhiping Shi Zhongzhi Shi Hongjian Sui Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

In our real world, there usually exist several different objects in one image, which brings intractable challenges to the traditional pattern recognition methods to classify the images. In this paper, we introduce a Conditional Random Fields (CRFs) model to deal with the Multi-label Image Classification problem. Considering the correlations of the objects, a second-order CRFs is constructed to capture the semantic associations between labels. Different initial feature weights are set to introduce the voting techniques for a better performance. We evaluate our methods on MSRC dataset and demonstrate high precision, recall and F 1 measure, showing that our method is competitive.

关键词： Visualization Training Image classification Correlation Buildings Image segmentation Semantics

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A parallel Hyper-Surface Classifier for high dimensional data

A parallel Hyper-Surface Classifier for high dimensional dat...

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International Symposium on Knowledge Acquisition and Modeling, KAM

作者： Qing He Qun Wang Chang-ying Du Xu-dong Ma Zhong-zhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing China

The enlarging volumes of data resources produced in real world makes classification of very large scale data a challenging task. Therefore, parallel process of very large high dimensional data is very important. Hyper-Surface Classification (HSC) is approved to be an effective and efficient classification algorithm to handle two and three dimensional data. Though HSC can be extended to deal with high dimensional data with dimension reduction or ensemble techniques, it is not trivial to tackle high dimensional data directly. Inspired by the decision tree idea, an improvement of HSC is proposed to deal with high dimensional data directly in this work. Furthermore, we parallelize the improved HSC algorithm (PHSC) to handle large scale high dimensional data based on MapReduce framework, which is a current and powerful parallel programming technique used in many fields. Experimental results show that the parallel improved HSC algorithm not only can directly deal with high dimensional data, but also can handle large scale data set. Furthermore, the evaluation criterions of scaleup, speedup and sizeup validate its efficiency.

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xu Han Zhongzhi Shi Wenjia Niu Kunrong Chen Xinghua Yang Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

With the rapid development of XML language which has good flexibility and interoperability, more and more log files of software running information are represented in XML format, especially for Web services. Fault diagnosis by analyzing semi-structured and XML like log files is becoming an important issue in this area. For most related learning methods, there is a basic assumption that training data should be in identical structure, which does not hold in many situations in practice. In order to learn from training data in different structures, we propose a similarity-based Bayesian learning approach for fault diagnosis in this paper. Our method is to first estimate similarity degrees of structural elements from different log files. Then the basic structure of combined Bayesian network (CBN) is constructed, and the similarity-based learning algorithm is used to compute probabilities in CBN. Finally, test log data can be classified into possible fault categories based on the generated CBN. Experimental results show our approach outperforms other learning approaches on those training datasets which have different structures.

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An Efficient Data Mining Framework on Hadoop using Java Persistence API

An Efficient Data Mining Framework on Hadoop using Java Pers...

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International Conference on Computer and information technology (CIT)

作者： Yang Lai Shi ZhongZhi Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. A feasible distributed data indexing algorithm is proposed for Hadoop data mining, based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format. A data mining framework on Hadoop using the Java Persistence API (JPA) and MySQL Cluster is proposed. The framework is elaborated in the implementation of a decision tree algorithm on Hadoop. We compare the data index-ing algorithm with Hadoop MapFile indexing, which performs a binary search, in a modest cloud environment. The results show the algorithm is more efficient than naïve MapFile indexing. We compare the JDBC and JPA implementations of the data mining framework. The performance shows the framework is efficient for data mining on Hadoop.

关键词： Data mining Indexing Distributed databases Pediatrics Clustering algorithms Decision trees

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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Fast object detection using boosted co-occurrence histograms of oriented gradients

Fast object detection using boosted co-occurrence histograms...

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IEEE International Conference on Image Processing

作者： Haoyu Ren Cher-Keng Heng Wei Zheng Luhong Liang Xilin Chen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Panasonic Singapore Laboratories Private Limited Singapore

Co-occurrence histograms of oriented gradients (CoHOG) are powerful descriptors in object detection. In this paper, we propose to utilize a very large pool of CoHOG features with variable-location and variable-size blocks to capture salient characteristics of the object structure. We consider a CoHOG feature as a block with a special pattern described by the offset. A boosting algorithm is further introduced to select the appropriate locations and offsets to construct an efficient and accurate cascade classifier. Experimental results on public datasets show that our approach simultaneously achieves high accuracy and fast speed on both pedestrian detection and car detection tasks.

关键词： Classification algorithms Training Support vector machines Feature extraction Accuracy Boosting Object detection

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