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222. A hybrid model for business failure prediction -- Utilization of particle swarm optimization and support vector machines

Creator:
Chen, Mu-Yen
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Particle swarm optimization, support vector machine, and business failure prediction
Language:
English
Description:
Bankruptcy has long been an important topic in finance and accounting research. Recent headline bankruptcies have included Enron, Fannie Mae, Freddie Mac, Washington Mutual, Merrill Lynch, and Lehman Brothers. These bankruptcies and their financial fallout have become a serious public concern due to huge influence these companies play in the real economy. Many researchers began investigating bankruptcy predictions back in the early 1970s. However, until recently, most research used prediction models based on traditional statistics. In recent years, however, newly-developed data mining techniques have been applied to various fields, including performance prediction systems. This research applies particle swarm optimization (PSO) to obtain suitable parameter settings for a support vector machine (SVM) model and to select a subset of beneficial features without reducing the classification accuracy rate. Experiments were conducted on an initial sample of 80 electronic companies listed on the Taiwan Stock Exchange Corporation (TSEC). This paper makes four critical contributions: (1) The results indicate the business cycle factor mainly affects financial prediction performance and has a greater influence than financial ratios. (2) The closer we get to the actual occurrence of financial distress, the higher the accuracy obtained both with and without feature selection under the business cycle approach. For example, PSO-SVM without feature selection provides 89.37% average correct cross-validation for two quarters prior to the occurrence of financial distress. (3) Our empirical results show that PSO integrated with SVM provides better classification accuracy than the Grid search, and genetic algorithm (GA) with SVM approaches for companies as normal or under threat. (4) The PSO-SVM model also provides better prediction accuracy than do the Grid-SVM, GA-SVM, SVM, SOM, and SVR-SOM approaches for seven well-known UCI datasets. Therefore, this paper proposes that the PSO-SVM approach could be a more suitable method for predicting potential financial distress.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

223. A hybrid texture analysis system based on non-linear & oriented kernels, particle swarm optimization, and kNN vs. support vector machines

Creator:
Peters, Stefanie and Koenig, Andreas
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Texture analysis, design automation, auto-configuration, PSO, and SVM
Language:
English
Description:
The presented work reports on the progress of our methodology and framework for automated image processing and analysis systém design for industrial vision application. We focus on the important task of automated texture analysis, which is an essential component of automated quality-control systems. In this context, the portfolio of texture operators and assessment methods has been enlarged. Optimized operator parameterization is investigated using particle swarm optimization (PSO). A particular goal of this work is the investigation of support vector machines (SVM) as alternative assessment method for the operator parameter optimization, incorporating the efficient inclusion of SVM parameter settings in this optimization. Methods of the enhanced portfolio were applied employing benchmark textures, real application data from leather inspection, and synthetic textures including defects, specially designed to industrial needs. The key results of our work are that SVM is a highly esteemed and powerful assessment and classification method and parameter optimization, based, e.g., on SVM/PSO of standard and proprietary texture operators boosted performance in all cases. However, the appropriateness of a certain operator proved to be highly data-dependent, which advocates our methodology even more. Thus, the operator selection has been included and investigated for the synthetic textures. Summarising, our work provides a generic texture analysis system, even for unskilled users, that is automatically configured to the application. The method portfolio will be enlarged in future work.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

224. A hybridized neuro-genetic solution for controlling industrial R³ workspace

Creator:
Irigoyen , E., Larrea, M., Valera, J., Gómez , V., and Artaza, F.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Hybrid neuro-genetic solution, optimal trajectory generation, multi-objective genetic algorithm, nonlinear neural control, and adaptive predictive control
Language:
English
Description:
This work presents a hybridized neuro-genetic control solution for R³ workspace application. The solution is based on a multi-objective genetic algorithm reference generator and an adaptive predictive neural network strategy. The trajectory calculation between two points in an R3 workspace is a complex optimization problem considering the fact that there are multiple objectives, restrictions and constraint functions which can play an important role in the problem and be in competition. We solve this problem using genetic algorithms, in a multi objective optimization strategy. Subsequently, we enhance a training algorithm in order to achieve the best adaptation of the neural network parameters in the controller which is responsible for generating the control action for a nonlinear system. As an application of the proposed hybridized control scheme, a crane tracking control is presented.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

225. A hyper-heuristic for adaptive scheduling in Computational Grids

Creator:
Xhafa, Fatos
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Hyper-heuristic, scheduling, Grid computing, heuristic methods, immediate mode, batch mode, and Grid simulator
Language:
English
Description:
In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in Computational Grids. An efficient scheduling of jobs to Grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of Grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

226. A knowledge based approach to dynamic route planning

Creator:
Eggenkamp , G. and Rothkrantz, L. J. M.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Dynamic routing, knowledge based systems, and artificial intelligence
Language:
English
Description:
In this paper, an expert system that performs route planning using dynamic traffic data is introduced. Also an algorithmic approach is introduced to find the shortest path in a three-dimensional. Using both implementations, a comparison is made between the expert system approach and the algorithmic approach. It is concluded that the expert system shows great potential. The expert system indeed finds the best routes, and it outperforms the algorithm approach in computation time, too.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

227. A Komlós-type theorem for the set-valued Henstock-Kurzweil-Pettis integral and applications

Creator:
Satco, Bianca
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Komlós convergence, Henstock-Kurzweil integral, Henstock-Kurzweil-Pettis set-valued integral, and selection
Language:
English
Description:
This paper presents a Komlós theorem that extends to the case of the set-valued Henstock-Kurzweil-Pettis integral a result obtained by Balder and Hess (in the integrably bounded case) and also a result of Hess and Ziat (in the Pettis integrability setting). As applications, a solution to a best approximation problem is given, weak compactness results are deduced and, finally, an existence theorem for an integral inclusion involving the Henstock-Kurzweil-Pettis set-valued integral is obtained.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

228. A Korovkin type approximation theorems via $\scr I$-convergence

Creator:
Duman, Oktay
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
$\scr{I}$-convergence, positive linear operator, and the classical Korovkin theorem
Language:
English
Description:
Using the concept of $\mathcal {I}$-convergence we provide a Korovkin type approximation theorem by means of positive linear operators defined on an appropriate weighted space given with any interval of the real line. We also study rates of convergence by means of the modulus of continuity and the elements of the Lipschitz class.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

229. A learning algorithm for a novel neural network architecture motivated by integrate-and-fire neuron model

Creator:
Mishra, Deepak, Yadav, Abhishek, and Kalra, Prem K.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Integrate-And-Fire Neuron Model, function approximation, classification, artificial neural network, and steepest descent method
Language:
English
Description:
In this paper, a learning algorithm for a novel neural network architecture motivated by Integrate-and-Fire Neuron Model (IFN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that inclusion of a few more biological phenomenon in the formulation of artificial neural networks make them more prevailing. Several benchmark and real-life problems of classification and function-approximation are illustrated.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

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