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Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
The algorithm of GASEN (Genetic Algorithm based Selective Ensemble Network) has been proven to be a very effective way to select a subset of neural networks to form an ensemble classifier or a regressor of enhanced generation ability. And yet performance of GASEN on class-imbalance data sets hasn't been discussed widely, while class-imbalance learning itself is an increasingly important issue. In...
Estimating the amount of effort required for developing a software system is one of the most important project management concerns. This study successfully produces an optimal reduced linear model for software cost estimation by employing a series of methods of multiple regressions to identify the most significant explanatory variables of the fifteen COCOMO cost drivers. The results yielded by the...
An algorithm for evolving recurrent neural network via the genetic algorithm was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary). Run on a Nvidia Tesla “GPU supercomputer, ” CuParcone achieves a performance increase of 323 times in face gender recognition compared to the comparable Parcone algorithm on a state-of-the-art, commodity...
In this paper, we propose A two-stage learning scheme for neural networks by integrating Gas into Structure identification In the first stage, which is also called structure identification stage, the selection of network structure and initial parameters is carried out by float genetic algorithm instead of human ln the second stage which is called parameter identification stage the conventional optimization...
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class...
Accurate classification of caller interactions within Interactive Voice Response systems would assist corporations to determine caller behavior within these telephony applications. This paper details the development of such a classification system for a pay beneficiary application. Fuzzy Inference Systems, Multi-Layer Perceptron, Support Vector Machine and ensemble of classifiers were developed. Accuracy,...
This paper discusses a feature extraction technique with genetic programming (GP) and bootstrap to improve interpretation accuracy of dissolved gas analysis (DGA) fault classification in power transformers, dealing with highly versatile or noise corrupted data. Initial DGA data are preprocessed with bootstrap to equalize the sample numbers for different fault classes, thus improving subsequent extraction...
Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy...
Online auctions have become extremely popular in recent years. Ability to predict winning bid prices accurately can help bidders to maximize their profit. This paper proposes a number of strategies and algorithms for performing such predictions for the first price sealed bid reverse auctions (FPSBRA). The neural networks (NN) and genetic programming (GP) learning techniques are used in the models...
Personal credit scoring plays an important role for commercial banks to keep away from consumer credit risks. This paper used neural networks for personal credit scoring and used two evolutional algorithms of genetic algorithm (GA) and particle swarm optimization (PSO) to train the networks to construct a GA neural network and a PSO neural network respectively. The two neural networks were used to...
Considering the issues that the sewage treatment process is a complicated and nonlinear system, it is very difficult to found the process model to describe it, and the key parameters of sewage treatment quality can not be detected on-line, a soft measurement modeling method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic...
Because of the complicated interaction of the sludge compost components, it makes the compost quality evaluation system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost quality evaluation modeling method based on high speed and precise genetic algorithm neural network is presented. The high speed and precise genetic algorithm neural network...
Building an accurate credit scoring model is very important to predict effectively the creditworthiness of new customers. Neural networks and genetic algorithm are suitable for building highly predictive credit scoring model, but the lack of transparency of these methods is a major drawback. On the other hand the main advantage of fuzzy models is their ability to describe the behavior of systems with...
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