Untitled

Tzallas

2023

Applied Sciences

The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets.

Section: Experimental Datasetsmentioning

confidence: 99%

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

Tzallas

2023

Applied Sciences

“…10. Ionosphere dataset, related to measurements from the ionosphere an thoroughly studied in a series of research papers [109,110]. 11.…”

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confidence: 99%

Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks

Tzallas

2023

Perhaps one of the best-known machine learning models is the artificial neural network, where a number of parameters must be adjusted to learn a wide range of practical problems from areas such as physics, chemistry, medicine, etc. Such problems can be reduced to pattern recognition problems and then modeled from artificial neural networks, whether these problems are classification problems or regression problems. To achieve the goal of neural networks, they must be trained by appropriately adjusting their parameters using some global optimization methods. In this work, the application of a recent global minimization technique is suggested for the adjustment of neural network parameters. In this technique, an approximation of the objective function to be minimized is created using artificial neural networks and then sampling is performed from the approximation function and not the original one. Therefore, in the present work, learning of the parameters of artificial neural networks is performed using other neural networks. The new training method was tested on a series of well-known problems, a comparative study was conducted against other neural network parameter tuning techniques, and the results were more than promising. From what was seen after performing the experiments and comparing the proposed technique with others that have been used for classification datasets as well as regression datasets, there was a significant difference in the performance of the proposed technique, starting with 30% for classification datasets and reaching 50% for regression problems. However, the proposed technique, because it presupposes the use of global optimization techniques involving artificial neural networks, may require significantly higher execution time than other techniques.

“…Ionosphere dataset. The ionosphere dataset contains data from the Johns Hopkins Ionosphere database and it has been studied in several papers [62,63]. 11.…”

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confidence: 99%

A Rule-Based Method to Locate the Bounds of Neural Networks

Tzallas²,

Karvounis³

2022

Knowledge

An advanced method of training artificial neural networks is presented here which aims to identify the optimal interval for the initialization and training of artificial neural networks. The location of the optimal interval is performed using rules evolving from a genetic algorithm. The method has two phases: in the first phase, an attempt is made to locate the optimal interval, and in the second phase, the artificial neural network is initialized and trained in this interval using a method of global optimization, such as a genetic algorithm. The method has been tested on a range of categorization and function learning data and the experimental results are extremely encouraging.