{"title":"A hybrid Support Vector Machine and autoregressive model for detecting gait disorders in the elderly","authors":"D. Lai, A. Khandoker, R. Begg, M. Palaniswami","doi":"10.1109/IJCNN.2007.4370976","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370976","url":null,"abstract":"The consequence of tripping and falling in the elderly population is serious because of the life threatening fractures which occur and the high medical costs incurred. Recently, the minimum toe clearance (MTC) has been employed in gait analysis as a sensitive gait variable for early detection of elderly people at risk of falling. In previous work, we successfully applied statistical and wavelet analysis methods with Support Vector Machines (SVM) to model the risk of tripping in the elderly. In this work, we propose to model the MTC time series as a wide based stationary random signal using the autoregressive (AR) process. Initially, it was found that a fourth order AR model constructed from 512 MTC samples per subject on 23 subjects completely modelled the balance impaired gait (pathological) from normal gait. However, when the number of MTC samples were reduced to 32, the two groups became inseparable. We then proposed a hybrid system consisting of a SVM classifier with AR model coefficients as input features to separate the two classes. It was found that SVMs with linear and Gaussian kernels produced 100% leave one out accuracies without the need for prior feature selection algorithms. In contrast, SVM models built previously from the best set of wavelet features produced only 86.95% leave one out accuracies. These results suggest that pathological gait is best modelled by the AR process if sufficient MTC data is available. In the case of shorter MTC data, the AR model still provides powerful and robust discriminative features which can be used by the SVM to detect elderly people at risk of falling.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132434817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Associative Memory for Association Rule Mining","authors":"Vicente Oswaldo Baez Monroy, Simon E. M. O'Keefe","doi":"10.1109/IJCNN.2007.4371304","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371304","url":null,"abstract":"Association rule mining is a thoroughly studied problem in data mining. Its solution has been aimed for by approaches based on different strategies involving, for instance, the use of novel data structures to represent the knowledge discovered, the transformation of the input data to speed up the process, the exploitation of the itemset properties either to traverse the possible itemset search space optimally or to form compact representation of the frequent itemsets employed for the generation of the corresponding final rules, and others. Surprisingly, biologically-inspired approaches have rarely been proposed. In this work, we focus on investigating if a type of mapping neural network, better known as an associative memory, is suitable for association rule mining. In particular, our aim is to determine if itemset support can be estimated from the knowledge embedded in the weight matrix of a trained associative memory in order to generate further association rules from such a knowledge.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132740291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Buried Underwater Object Classification Using a Collaborative Multi-Aspect Classifier","authors":"J. Cartmill, M. Azimi-Sadjadi, N. Wachowski","doi":"10.1109/IJCNN.2007.4371232","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371232","url":null,"abstract":"In this paper, a new collaborative multi-aspect classification system (CMAC) is introduced. CMAC utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on features obtained over multiple aspects. This system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multi-ping classification of mine-like objects while simultaneously reducing the number of false alarms compared to a multi-ping decision-level fusion classifier.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132742951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Intelligent Tracking Controller Design Using Dynamic Fuzzy Neural Networks","authors":"Chun-Fei Hsu, Tsu-Tian Lee, Ping-Zong Lin","doi":"10.1109/IJCNN.2007.4370946","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370946","url":null,"abstract":"An intelligent tracking control using a dynamic fuzzy neural network (DFNN) is proposed in this paper. The intelligent tracking control system is comprised of a computation controller and a robust controller. The computation controller containing a DFNN identifier is the principal controller, and the robust controller is designed to achieve L 2 tracking performance. The DFNN identifier uses the structure and parameter learning phases to online estimate the unknown control dynamics equation. Finally, the proposed intelligent tracking control system is applied to control a second-order chaotic circuit system. The simulation results show that the proposed intelligent tracking control system can achieve favorable tracking performance by incorporating of DFNN identification, sliding-mode control and robust control techniques.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"343 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133182944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines","authors":"G. Cawley, N. L. C. Talbot","doi":"10.1109/IJCNN.2007.4371219","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371219","url":null,"abstract":"The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best possible performance is therefore largely a matter of the design of a good kernel for the problem at hand, exploiting any underlying structure and optimisation of the regularisation and kernel parameters, i.e. model selection. Fortunately, analytic bounds on, or approximations to, the leave-one-out cross-validation error are often available, providing an efficient and generally reliable means to guide model selection. However, the degree to which the incorporation of prior knowledge improves performance over that which can be obtained using \"standard\" kernels with automated model selection (i.e. agnostic learning), is an open question. In this paper, we compare approaches using example solutions for all of the benchmark tasks on both tracks of the IJCNN-2007 Agnostic Learning versus Prior Knowledge Challenge.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133227323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Pseudo RBF Network for Position Independent Hand Posture Recognition System","authors":"H. Hikawa, Shigeki Matsubara","doi":"10.1109/IJCNN.2007.4371103","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371103","url":null,"abstract":"This paper proposes a new neuron architecture for a network similar to the radial basis function (RBF) network. The network with the proposed neuron, which we call a pseudo RBF network, is aimed for pattern classifications. Same as the conventional RBF network, each neuron in the hidden layer of the network is associated with a single cluster that represents a subclass. The proposed neuron effectively evaluates the possibility of the input vector belonging to its cluster. The pseudo RBF network with the proposed neuron is applied to a hand posture recognition system. Input image is preprocessed through horizontal/vertical projection followed by discrete Fourier transforms (DFTs) that calculate the magnitude spectrum. The magnitude spectrum is used as the feature vector to be fed to the network. Use of the magnitude spectrum makes the system very robust against the position changes of the hand image. The simulation results show that the average recognition rate of the system is 98% even though the hand positions are changed randomly.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Hammer, A. Hasenfuss, Frank-Michael Schleif, T. Villmann, M. Strickert, U. Seiffert
{"title":"Intuitive Clustering of Biological Data","authors":"B. Hammer, A. Hasenfuss, Frank-Michael Schleif, T. Villmann, M. Strickert, U. Seiffert","doi":"10.1109/IJCNN.2007.4371244","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371244","url":null,"abstract":"K-means clustering combines a variety of striking properties because of which it is widely used in applications: training is intuitive and simple, the final classifier represents classes by geometrically meaningful prototypes, and the algorithm is quite powerful compared to more complex alternative clustering algorithms. In this contribution, we focus on extensions which incorporate additional information into the clustering algorithm to achieve a better accuracy: neighborhood cooperation from neural gas, (possibly fuzzy) label information of input data, and general problem-adapted distances instead of the standard Euclidean metric. These extensions can be formulated in a simple general framework by means of a cost function. We demonstrate the ability of these variants on several representative clustering problems from computational biology.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131689422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Lisboa, S. Bonnevay, S. Négrier, T. Etchells, I. Jarman, M.S. Hane Aung, S. Chabaud, T. Bachelot, D. Pérol, T. Gargi, V. Bourdès
{"title":"Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer","authors":"P. Lisboa, S. Bonnevay, S. Négrier, T. Etchells, I. Jarman, M.S. Hane Aung, S. Chabaud, T. Bachelot, D. Pérol, T. Gargi, V. Bourdès","doi":"10.1109/IJCNN.2007.4371357","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371357","url":null,"abstract":"This paper presents an analysis of censored survival data for breast cancer specific mortality and disease free survival. There are three stages to the process, namely time-to-event modelling, risk stratification by predicted outcome and model interpretation using rule extraction. Model selection was carried out using the benchmark linear model, Cox regression but risk staging was derived with Cox regression and with Partial Logistic Regression Artificial Neural Networks regularised with Automatic Relevance Determination (PLANN-ARD). This analysis compares the two approaches showing the benefit in using the neural network framework is better specificity for patients at high risk. The neural network model also has results in a smooth model of the hazard without the need for limiting assumptions of proportionality. The model predictions were verified using out-of-sample testing and by comparing marginal estimates of the predicted and actual cumulative hazards. The analysis was extended with automatic rule generation using Orthogonal Search Rule Extraction (OSRE). This methodology translates analytical risk scores into the language of the clinical domain, enabling direct validation of the operation of the Cox or neural network model.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115463769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Fiannaca, G. D. Fatta, R. Rizzo, A. Urso, S. Gaglio
{"title":"Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds","authors":"A. Fiannaca, G. D. Fatta, R. Rizzo, A. Urso, S. Gaglio","doi":"10.1109/IJCNN.2007.4371399","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371399","url":null,"abstract":"Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen's self organizing map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the high throughput screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124108992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Off-line Signature Verification Using Writer-Independent Approach","authors":"Luiz Oliveira, E. Justino, R. Sabourin","doi":"10.1109/IJCNN.2007.4371358","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371358","url":null,"abstract":"In this work we present a strategy for off-line signature verification. It takes into account a writer-independent model which reduces the pattern recognition problem to a 2-class problem, hence, makes it possible to build robust signature verification systems even when few signatures per writer are available. Receiver operating characteristic (ROC) curves are used to improve the performance of the proposed system . The contribution of this paper is two-fold. First of all, we analyze the impacts of choosing different fusion strategies to combine the partial decisions yielded by the SVM classifiers. Then ROC produced by different classifiers are combined using maximum likelihood analysis, producing an ROC combined classifier. Through comprehensive experiments on a database composed of 100 writers, we demonstrate that the ROC combined classifier based on the writer-independent approach can reduce considerably false rejection rate while keeping false acceptance rates at acceptable levels.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124382851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}