KEDRI - the Knowledge Engineering and Discovery Research Institute

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https://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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    DENFIS: dynamic evolving neural-fuzzy inference system and its application for time series prediction
    (IEEE, 2002) Kasabov, N; Song, Q.
    This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
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    Neural Systems for solving the inverse problem of recovering the Primary Signal Waveform in potential transformers
    (IEEE, 2003) Kasabov, N; Venkov, G.; Minchev, S.
    The inverse problem of recovering the potential transformer primary signal waveform using secondary signal waveform and information about the secondary load is solved here via two inverse neural network models. The first model uses two recurrent neural networks trained in an off-line mode. The second model is designed with the use a Dynamic Evolving Neural-Fuzzy Interface System (DENFIS) and suited for on-line application and integration into existing protection algorithms as a parallel module. It has the ability of learning and adjusting its structure in an on-line mode to reflect changes in the environment. The model is suited for real time applications and improvement of protection relay operation. The two models perform better than any existing and published models so far and are useful not only for the reconstruction of the primary signal, but for predicting the signal waveform for some time steps ahead and thus for estimating the drifts in the incoming signals and events.
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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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    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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    An adaptive model of person identification combining speech and image information
    (IEEE, 2004) Zhang, D.; Ghobakhlou, A.; Kasabov, N
    The paper introduces a combination of adaptive neural network systems and statistical method for integrating speech and face image information for person identification. The method allows for the development of models of persons and their on-going adjustment based on new speech and face images. The method is illustrated with a modeling and classification of different persons, when speech and face images are presented in an incremental way. In this model, there are two sub - networks, one for face image and one for speaker recognition. A higher-level layer is applied to make a final decision. In the speaker recognition subnetwork, a text-dependant model is built using Evolving Connectionist Systems (ECOS) [1]. In the face image recognition sub-network, composite profile technique is applied for face image feature extraction and Zero Instruction Set Computing (ZISC) [2] technology is used to build the neural network. In the higher-level conceptual subsystem, final recognition decision is made using statistical method. The experiments show that ECOS and ZISC are appropriate techniques for the creation of evolving models for the task of speaker and face recognition individually. It is also shown that the integration of the speech and image information using statistical method improves the person identification rate. © 2004 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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    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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    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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    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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    Computational neurogenetic modelling: gene networks within neural networks
    (IEEE, 2004) Kasabov, N; Benuskova, L.; Gomes Wysoski, S.
    This paper introduces a novel connectionist approach to neural network modelling that integrates dynamic gene networks within neurons with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network. Through tuning the gene interaction network and the initial gene/protein expression values, different states of the neural network operation can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). Functioning of the SNN can be evaluated for instance by the field potentials, thus making it possible to attempt modelling the role of genes in different brain states such as epilepsy, schizophrenia, and other states, where EEG data is available to test the model predictions.
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    Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems
    (IEEE, 2004) Pang, S.; Kasabov, N
    This paper compares inductive-, versus transductive modeling, and also global-, versus local models with the use of SVM for gene expression classification problems. SVM are used in their three variants - inductive SVM, transductive SVM (TSVM), and SVM tree (SVMT) -the last two techniques being recently introduced by the authors. The problem of gene expression classification is used for illustration and four benchmark data sets are used to compare the different SVM methods. The TSVM outperforms the inductive SVM models applied on a small to medium variable (gene) set and a small to medium sample set, while SVMT is superior when the problem is defined with a large data set, or - a large set of variables (e.g. 7,000 genes, with little or no variable pre-selection).
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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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    Efficient global clustering using the greedy elimination method
    (IEEE, 2004) Chan, Z.; Kasabov, N
    A novel global clustering method called the greedy elimination method is presented. Experiments show that the proposed method scores significantly lower clustering errors than the standard K-means over two benchmark and two application datasets, and it is efficient for handling large datasets.
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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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    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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    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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    Incremental Linear Discriminant analysis for classification of Data Streams
    (IEEE, 2005) Pang, S.; Ozawa, S.; Kasabov, N
    This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods. © 2005 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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    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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