KEDRI - the Knowledge Engineering and Discovery Research Institute

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http://hdl.handle.net/10292/552
KEDRI - the Knowledge Engineering and Discovery Research Institute of Auckland University of Technology was established in June 2002 and since then has been developing novel information processing methods, technologies and their applications to enhance discoveries across different areas of science and engineering. The methods are mainly based on principles from Nature, such as brain information processing, evolution, genetics, quantum physics.

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    Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics
    (IEEE, 2003) Kasabov, N; Song, Q.; Nishikawa, I.
    The paper describes a method of using evolutionary computation technique for parameter optimisation of evolving connectionist systems (ECOS) that operate in an online, life-long learning mode. ECOS evolve their structure and functionality from an incoming stream of data in either a supervised-, or/and in an unsupervised mode. The algorithm is illustrated on a case study of predicting a chaotic time-series that changes its dynamics over time. With the on-line parameter optimisation of ECOS, a faster adaptation and a better prediction is achieved. The method is practically applicable for real time applications.
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    An incremental principal component analysis for chunk data
    (IEEE, 2006) Ozawa, S.; Pang, S.; Kasabov, N
    This paper presents a new algorithm of dynamic feature selection by extending the algorithm of Incremental Principal Component Analysis (IPCA), which has been originally proposed by Hall and Martin. In the proposed IPCA, a chunk of training samples can be processed at a time to update the eigenspace of a classification model without keeping all the training samples given so far. Under the assumption that L of training samples are given in a chunk, first we derive a new eigenproblem whose solution gives us a rotation matrix of eigen-axes, then we introduce a new algorithm of augmenting eigen-axes based on the accumulation ratio. We also derive the one-pass incremental update formula for the accumulation ratio. The experiments are carried out to verify if the proposed IPCA works well. Our experimental results demonstrate that it works well independent of the size of data chunk, and that the eigenvectors for major components are obtained without serious approximation errors at the final learning stage. In addition, it is shown that the proposed IPCA can maintain the designated accumulation ratio by augmenting new eigen-axes properly. This property enables a learning system to construct an informative eigenspace with minimum dimensionality. © 2006 IEEE.
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    WDN-RBF: weighted data normalization for radial basic function type neural networks
    (IEEE, 2004) Song, Q.; Kasabov, N
    This paper introduces an approach of Weighted Data Normalization (WDN) for Radial Basis Function (RBF) type of neural networks. It presents also applications for medical decision support systems. The WDN method optimizes the data normalization ranges for the input variables of the neural network. A steepest descent algorithm (BP) is used for the WDN-RBF learning. The derived weights have the meaning of feature importance and can be used to select a minimum set of variables (features) that can optimize the performance of the RBF network model. The WDN-RBF is illustrated on two case study prediction/identification problems. The first one is prediction of the Mackey-Glass time series and the second one is a real medical decision support problem of estimating the level of renal functions in patients. The method can be applied to other distance-based, prototype learning neural network models.
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    NFI: a neuro-fuzzy inference method for transductive reasoning
    (IEEE, 2005) Song, Q.; Kasabov, N
    This paper introduces a novel neural fuzzy inference method - NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS - dynamic neuro-fuzzy inference systems for both online and offline time series prediction tasks. While inductive reasoning is concerned with the development of a model (a function) to approximate data in the whole problem space (induction), and consecutively - using this model to predict output values for a new input vector (deduction), in transductive reasoning systems a local model is developed for every new input vector, based on some closest to this vector data from an existing database (also generated from an existing model). NFI is compared with both inductive connectionist systems (e.g., MLP, DENFIS) and transductive reasoning systems (e.g., K-NN) on three case study prediction/identification problems. The first one is a prediction task on Mackey Glass time series; the second one is a classification on Iris data; and the last one is a real medical decision support problem of estimating the level of renal function of a patient, based on measured clinical parameters for the purpose of their personalised treatment. The case studies have demonstrated better accuracy obtained with the use of the NFI transductive reasoning in comparison with the inductive reasoning systems. © 2005 IEEE.
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    Computational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations
    (IEEE, 2006) Benuskova, L.; Wysoski, S.; Kasabov, N
    We present new results from Computational Neurogenetic Modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole neural network model through neuronal parameters, which change their values as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal parameters, particular target states of the neural network operation can be achieved, and statistics about gene interaction matrix can be extracted. In such a way it is possible to model the role of genes and their interactions in different brain states and conditions. Experiments with human EEG data are presented as an illustration of this methodology and also, as a source for the discovery of unknown interactions between genes in relation to their impact on brain activity. © 2006 IEEE.
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    On-line evolving fuzzy clustering
    (IEEE, 2007) Ravi, V.; Srinivas, E.; Kasabov, N
    In this paper, a novel on-line evolving fuzzy clustering method that extends the evolving clustering method (ECM) of Kasabov and Song (2002) is presented, called EFCM. Since it is an on-line algorithm, the fuzzy membership matrix of the data is updated whenever the existing cluster expands, or a new cluster is formed. EFCM does not need the numbers of the clusters to be pre-defined. The algorithm is tested on several benchmark data sets, such as Iris, Wine, Glass, E-Coli, Yeast and Italian Olive oils. EFCM results in the least objective function value compared to the ECM and Fuzzy C-Means. It is significantly faster (by several orders of magnitude) than any of the off-line batch-mode clustering algorithms. A methodology is also proposed for using theXie-Beni cluster validity measure to optimize the number of clusters. © 2007 IEEE.
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    Evolving connectionist systems based role allocation of robots for soccer playing
    (IEEE, 2005) Huang, L.; Song, Q.; Kasabov, N
    For a group of robots (multi-agents) to complete a task, it is important for each of them to play a certain role changing with the environment of the task. One typical example is robotic soccer in which a team of mobile robots perform soccer playing behaviors. Traditionally, a robot's role is determined by a closed-form function of a robot's postures relative to the target which usually cannot accurately describe real situations. In this paper, the robot role allocation problem is converted to the one of pattern classification. Evolving classification function (ECF), a special evolving connectionist systems (ECOS), is used to identify the suitable role of a robot from the data collected from the robot system in real time. The software and hardware platforms are established for data collection, learning and verification for this approach. The effectiveness of the approach are verified by the experimental studies. ©2005 IEEE.
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    A two-stage methodology for gene regulatory network extraction from time-course gene expression data
    (IEEE, 2004) Chan, Z.; Kasabov, N; Collins, L.
    The discovery of gene regulatory networks (GRN) from time-course gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function; (2) gaining an understanding on the dynamic interaction between genes; (3) predicting gene expression values at future time points and accordingly, (4) predicting drug effect over time. In this paper, we propose a two-stage methodology that is implemented in the software "Gene Network Explorer (GNetXP)" for extracting GRNs from gene trajectory data. In the first stage, we apply a hybrid Genetic Algorithm and Expectation Maximization algorithm on clustering the large number of gene trajectories using the mixture of multiple linear regression models for fitting the trajectory data. In the second stage, we apply the Kalman Filter to identify a set of first-order differential equations that describe the dynamics of the representative trajectories, and use these equations for discovering important gene interactions and predicting gene expression values at future time points. The proposed method is demonstrated on the human fibroblast response gene expression data. ©2004 IEEE.
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    A computational neurogenetic model of a spiking neuron
    (IEEE, 2005) Kasabov, N; Benuskova, L.; Wysoski, S.
    The paper presents a novel, biologically plausible spiking neuronal model that includes a dynamic gene network. Interactions of genes in neurons affect the dynamics of the neurons and the whole network through neuronal parameters that change as a function of gene expression. The proposed model is used to build a spiking neural network (SNN) illustrated on a real EEC data case study problem. The paper also presents a novel computational approach to brain neural network modeling that integrates dynamic gene networks with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network through neuronal parameters, which are no longer constant, but change as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and ANN parameters, particular target states of the neural network operation can be achieved, and statistics about gene intercation matrix can be extracted. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). The behavior of SNN is evaluated by means of the local field potential, thus making it possible to attempt modeling the role of genes in different brain states, where EEC data is available to test the model. We use standard signal processing techniques like FFT to evaluate the SNN output to compare it with real human EEC data. © 2005 IEEE.
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    TWNFC - Transductive neural-fuzzy classifier with weighted data normalization and its application in medicine
    (IEEE, 2005) Ma, T.; Song, Q.; Marshall, M.; Kasabov, N
    This paper introduces a novel fuzzy model - transductive neural-fuzzy classifier with weighted data normalization (TWNFC), While inductive approaches are concerned with the development of a model to approximate data in the whole problem space (induction), and consecutively - using this model to calculate the output value(s) for a new input vector (deduction), in transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. The weighted data normalization method (WDN) optimizes the data normalization ranges for the input variables of a system. A steepest descent algorithm is used for training the TWNFC model The TWNFC is illustrated on a case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This personalized modeling can also be applied to other distance-based, prototype learning neural network or fuzzy inference models. © 2005 IEEE.
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    Brain-gene ontology: integrating bioinformatics and neuroinformatics data, information and knowledge to enable discoveries
    (IEEE, 2006) Kasabov, N; Jain, V.; Gottgtroy, P.; Benuskova, L.; Joseph, F.
    The paper presents some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. BGO includes various concepts, facts, data, software simulators, graphs, videos, animations, and other information forms, related to brain functions, brain diseases, their genetic basis and the relationship between all of them. The first version of the brain-gene ontology has been completed as a hierarchical structure and as an initial implementation in the Protégé ontology building environment.
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    Integrated Gene Expression analysis of Multiple Microarray data sets based on a Normalization Technique and on Adaptive Connectionist model
    (IEEE, 2003) Goh, L.; Kasabov, N
    Research with microarray gene expression analysis has primarily been on expression profiling based on one set of microarray data. This paper presents a novel approach to integrated analysis and modeling of microarray data from multiple sources. Normalization method is applied to different data sets before they are used together in an adaptive connectionist classification system. The method is demonstrated on a bench-mark case study problem of classifying Diffuse Large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL). For the purpose of comparison, different normalization techniques were applied and connectionist models were created from one or more microarray data sets and then tested on the others. The results show that with the use of proper normalization and modeling techniques, a model based on one set of data can be used to classify microarray data from totally different sources. For the modeling part, evolving connectionist systems (ECOS) are used that allow for new data to be added in an incremental way so that connectionist systems can be built for on-line adaptive learning where new data from various sources can be added into the system.
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    Neuro-, genetic-, and quantum inspired evolving intelligent systems
    (IEEE, 2006) Kasabov, N
    This paper discusses opportunities and challenges for the creation of evolving artificial neural network (ANN) and more general - computational intelligence (CI) models inspired by principles at different levels of information processing in the brain - neuronal-, genetic-, and quantum, and mainly - the issues related to the integration of these principles into more powerful and accurate ANN models. A particular type of ANN, evolving connectionist systems (ECOS), is used to illustrate this approach. ECOS evolve their structure and functionality through continuous learning from data and facilitate data and knowledge integration and knowledge elucidation. ECOS gain inspiration from the evolving processes in the brain. Evolving fuzzy neural networks and evolving spiking neural networks are presented as examples. With more genetic information available now, it becomes possible to integrate the gene and the neuronal information into neuro-genetic models and to use them for a better understanding of complex brain processes. Further down in the information processing hierarchy, are the quantum processes. Quantum inspired ANN may help solve efficiently the hardest computational problems. It may be possible to integrated quantum principles into brain-gene inspired ANN models for a faster and more accurate modeling. All the topics above are illustrated with some contemporary solutions, but many more open questions and challenges are raised and directions for further research outlined. © 2006 IEEE.
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    Transductive modeling with GA parameter optimization
    (IEEE, 2005) Mohan, N.; Kasabov, N
    Introduction - While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. The model approximates the output value only for this input vector. However, deciding on the appropriate distance measure, on the number of nearest neighbors and on a minimum set of important features/variables is a challenge and is usually based on prior knowledge or exhaustive trial and test experiments. This paper proposes a Genetic Algorithm (GA) approach for optimizing these three factors. The method is tested on several datasets from UCI repository for classification tasks and results show that it outperforms conventional approaches. The drawback of this approach is the computational time complexity due to the presence of GA, which can be overcome using parallel computer systems due to the intrinsic parallel nature of the algorithm. © 2005 IEEE.
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    Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition
    (IEEE, 2004) Kasabov, N; Pang, S.
    This paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is concerned with the development of a model (a function) to approximate data from the whole problem space (induction), and consecutively using this model to predict output values for a new input vector (deduction), in the transductive inference systems a model is developed for every new input vector based on some closest to the new vector data from an existing database and this model is used to predict only the output for this vector. The TSVM outperforms by far the inductive SVM models applied on the same problems. Analysis is given on the advantages and disadvantages of the TSVM. Hybrid TSVM-evolving connections systems are discussed as directions for future research.
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    Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition
    (IEEE, 2005) Kasabov, N; Zhang, D.; Pang, P.
    The paper presents an integrated approach to incremental learning in autonomous systems, that includes both pattern recognition and feature selection. The approach utilizes evolving connectionist systems (ECoS) and is applied on on-line image and speech pattern learning and recognition tasks. The experiments show that ECoS are a suitable paradigm for building autonomous systems for learning and navigation in a new environment using both image and speech modalities. © 2005 IEEE.
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    Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method
    (IEEE, 2004) Chan, Z.; Kasabov, N
    Clustering time course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network (GRN) modeling and discovery as it significantly reduces the dimensionality of the gene space required for analysis. This paper introduces a novel method that hybridizes Genetic Algorithm (GA) and Expectation Maximization algorithms (EM) for clustering with the mixtures of Multiple Linear Regression models (MLRs). The proposed method is applied to cluster gene expression time course data into smaller number of classes based on their trajectory similarities. Its performance and application as a generic clustering method to other complex problems are discussed.
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    A versatile quantum-inspired evolutionary algorithm
    (IEEE, 2007-09-25) Platel, M.; Sehliebs, S.; Kasabov, N
    This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called Versatile Quantum-inspired Evolutionary Algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a Classical Genetic Algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to Estimation of Distribution Algorithms (EDA). © 2007 IEEE.
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    Bioinformatics: a knowledge engineering approach
    (IEEE, 2004) Kasabov, N
    The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in bioinformatics are given, that include: DNA sequence analysis, microarray gene expression profiling, protein structure prediction, finding gene regulatory networks, medical prognostic systems, computational neurogenetic modeling.
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