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An n-spheres based synthetic data generator for supervised classification

An n-spheres based synthetic data generator for supervised c...

引用

12th International Work-Conference on Artificial Neural Networks, IWANN 2013

作者： Sánchez-Monedero, Javier Gutiérrez, Pedro Antonio Pérez-Ortiz, María Hervás-Martínez, César Dept. of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642386787

Synthetic datasets can be useful in a variety of situations, specifically when new machine learning models and training algorithms are developed or when trying to seek the weaknesses of an specific method. In contrast to real-world data, synthetic datasets provide a controlled environment for analysing concrete critic points such as outlier tolerance, data dimensionality influence and class imbalance, among others. In this paper, a framework for synthetic data generation is developed with special attention to pattern order in the space, data dimensionality, class overlapping and data multimodality. Variables such as position, width and overlapping of data distributions in the n-dimensional space are controlled by considering them as n-spheres. The method is tested in the context of ordinal regression, a paradigm of classification where there is an order arrangement between categories. The contribution of the paper is the full control over data topology and over a set of relevant statistical properties of the data. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Classification (of information)

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Borderline kernel based over-sampling

Borderline kernel based over-sampling

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8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013

作者： Pérez-Ortiz, María Gutiérrez, Pedro Antonio Hervás-Martínez, César University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642408458

Nowadays, the imbalanced nature of some real-world data is receiving a lot of attention from the pattern recognition and machine learning communities in both theoretical and practical aspects, giving rise to different promising approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with kernel classifiers, that operate in the feature space induced by a kernel function. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space and therefore preserves its structure) to derive a kernel-based synthetic over-sampling technique based on borderline instances which are considered as crucial for establishing the decision boundary. Therefore, the proposed methodology would maintain the main properties of the kernel mapping while reinforcing the decision boundaries induced by a kernel machine. The results show that the proposed method achieves better results than the same borderline over- sampling method applied in the original input space. © 2013 Springer-Verlag.

关键词： Pattern recognition

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A novel component identification approach using evolutionary programming

A novel component identification approach using evolutionary...

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Ramírez, Aurora Romero, José Raúl Ventura, Sebastián Dept. of Computer Science and Numerical Analysis Rabanales Campus University of Córdoba 14071 Córdoba Spain

ISBN: (纸本)9781450319645

Component identification is a critical phase in software architecture analysis to prevent later errors and control the project time and budget. Obtaining the most appropriate architecture according to predetermined design criteria can be treated as an optimization problem, especially since the appearance of the Search Based Software Engineering, and its combination with bio-inspired metaheuristics. In this work, an evolutionary programming (EP) algorithm is used to identify components, based on a novel and comprehensible representation of software architectures.

关键词： Budget control

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Experimental verification of a new analytical procedure to design a compact bandpass filters for ISM and WiMAX applications 14

Experimental verification of a new analytical procedure to d...

引用

2014 14th Mediterranean Microwave Symposium, MMS 2014

作者： Latioui, Hafssaa Alejos, Ana Vazquez Aghzout, Otman Sanchez, Manuel Garcia Naghar, Azzeddin Essaaidi, Mohamed Faculty of Science National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Dept. of Signal Theory and Communication University of Vigo Pontevedra Vigo Spain National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Faculty of Science Abdelmalek Essaadi University Tetouan Morocco National School of Computer Science and Systems Analysis Mohamed V-Souissi Rabat Morocco

ISBN: (纸本)9781479973910

This paper presents a new efficient numerical formulations procedure to predict the responses of a N-poles Chebyshev parallel-coupled Microstrip bandpass filter for wireless communication technologies. Based on the transmission line theory approach, these numerical formulations have been developed using the mathematical analysis concept to predict the desired s-parameters of the desired filter. To validate the theoretical method developed in this work, a third and a second order parallel coupled microstrip band pass filters for WiMAX and ISM band respectively have been fabricated and measured. The measured scattering-parameters show a very good agreement with predictions by the theoretical responses, which validates the design procedure of the proposed filter. © 2014 IEEE.

关键词： Scattering parameters

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A simple graphical calculator based on a new synthesis formulas to design a bandpass filters for wireless applications 14

A simple graphical calculator based on a new synthesis formu...

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2014 14th Mediterranean Microwave Symposium, MMS 2014

作者： Latioui, Hafssaa Aghzout, Otman Naghar, Azzeddin Alejos, Ana Vazquez Sanchez, Manuel Garcia Essaaidi, Mohamed Faculty of Science National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Faculty of Science Abdelmalek Essaadi University Tetouan Morocco Dept. of Signal Theory and Communication University of Vigo Pontevedra Vigo Spain National School of Computer Science and Systems Analysis Mohamed V-Souissi Rabat Morocco

ISBN: (纸本)9781479973910

On the one hand this paper presents a theoretical method to predict the responses for the parallel coupled microstrip bandpass filters, and on the other hand proposes a new MATLAB simulation interface including all parameters design procedure to predict the filter responses. The main advantage of this developed interface calculator is to enable researchers and engineers to design and determine easily all parameters of the PCMBPF responses with high accuracy and very small CPU time. To validate the numerical method and the corresponding new interface calculator, two PCMBP filters for wireless communications are designed and compared with the commercial electromagnetic CST simulator and the fabricated prototype respectively. Measured results show good agreement with those obtained by numerical method and simulations. © 2014 IEEE.

关键词： numerical methods

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Synthetic over-sampling in the empirical feature space

Synthetic over-sampling in the empirical feature space

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21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Hervás-Martínez, C. University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus Albert Einstein building 14071 Córdoba Spain

ISBN: (纸本)9782874190810

The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to different approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with the kernel classifiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space) to develop a kernel-based synthetic over-sampling technique, which maintains the main properties of the kernel mapping. The proposal achieves better results than the same oversampling method applied to the original input space.

关键词： Learning systems

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Multi-scale support vector machine optimization by kernel target-alignment

Multi-scale support vector machine optimization by kernel ta...

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21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Sánchez-Monedero, J. Hervás-Martínez, C. University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus Albert Einstein building 14071 - Córdoba Spain

ISBN: (纸本)9782874190810

The problem considered is the optimization of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, and through the use of evolutionary or gradient descent approaches, which explicitly train the learning machine and thereby incur high computacional cost. To cope with this limitation, the problem is explored by making use of an analytical methodology known as kernel-target alignment, where the kernel is optimized by aligning it to the so-called ideal kernel matrix. The results show that the proposal leads to better performance and simpler models at limited computational cost when applying the binary Support Vector Machine (SVM) paradigm.

关键词： Optimization

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Spatial discriminant ICA for RS-fMRI characterisation

Spatial discriminant ICA for RS-fMRI characterisation

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International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Alejandro Tabas Emili Balaguer-Ballester Laura Igual Faculty of Science and Technology Bournemouth University Faculty of Science and Technology Bournemouth University UK Berstein Center for Comp. Neuroscience Dept. of Applied Mathematics & Analysis Universitat de Barcelona Computer Vision Center of Barcelona

Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher's Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.

关键词： Algorithm design and analysis Data mining Noise Brain Independent component analysis Standards computer architecture

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A Multithreaded Bound Varying Chaotic Firefly Algorithm for prime factorization

A Multithreaded Bound Varying Chaotic Firefly Algorithm for ...

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IEEE International Advance Computing Conference, IACC

作者： Mohit Mishra Utkarsh Chaturvedi Saibal K. Pal Dept. of Computer Science and Engineering Indian Institute of Technology (Banaras Hindu University) Varanasi India Scientific Analysis Group (SAG) Defence Research & Development Organization (DRDO) New Delhi India

ISBN: (纸本)9781479925735

Factorization of a number composed of two large prime numbers of almost equal number of digits is computationally a difficult task. The RSA public-key cryptosystem relies on this difficulty of factoring out the product of two very large prime numbers. There are various ways to find these two prime factors, but the huge memory and runtime expenses for large numbers pose tremendous difficulty. In this paper, we explore the possibility of solving this problem with the aid of Swarm Intelligence Metaheuristics using a Multithreaded Bound Varying Chaotic Firefly Algorithm. Firefly algorithm is one of the recent evolutionary computing models inspired by the behavior of fireflies. We have considered factors of equal number of digits. Observations show that the Firefly algorithm can be an effective tool to factorize a semi prime and hence can be further extended on extremely large numbers.

关键词： Decision support systems Conferences

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Advances in Bayesian network learning using integer programming

Advances in Bayesian network learning using integer programm...

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29th Conference on Uncertainty in Artificial Intelligence, UAI 2013

作者： Barlett, Mark Cussens, James Dept. of Computer Science York Centre for Complex Systems Analysis University of York United Kingdom

We consider the problem of learning Bayesian networks (BNs) from complete discrete data. This problem of discrete optimisation is formulated as an integer program (IP). We describe the various steps we have taken to allow efficient solving of this IP. These are (i) efficient search for cutting planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal) BNs and (iii) tightening the linear relaxation of the IP. After relating this BN learning problem to set covering and the multidimensional 0-1 knapsack problem, we present our empirical results. These show improvements, sometimes dramatic, over earlier results.

关键词： Integer programming

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