Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

A generic optimising feature extraction method using multiobjective genetic programming

In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved

Competitively Evolving Decision Trees Against Fixed Training Cases for Natural Language Processing

Competitive fitness functions can generate performance superior to absolute fitness functions [Angelineand Pollack 1993], [Hillis 1992]. This chapter describes a method by which competition can be implemented when training over a fixed (static) set of examples. Since new training cases cannot be generated by mutation or crossover, the probabilistic frequencies by which individual training cases are selected competitively adapt. We evolve decision trees for the problem of word sense disambiguation. The decision trees contain embedded bit strings; bit string crossover is intermingled with subtree-swapping. To approach the problem of overlearning, we have implemented a fitness penalty function specialized for decision trees which is dependent on the partition of the set of training cases implied by a decision tree

Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function

This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy

Semantic Segmentation Network Stacking with Genetic Programming

Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Semantic Segmentation Network Stacking with Genetic Programming. Genetic Programming And Evolvable Machines, 24(2 — Special Issue on Highlights of Genetic Programming 2022 Events), 1-37. [15]. https://doi.org/10.1007/s10710-023-09464-0---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), AICE (DSAIPA/DS/0113/2019), UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, and by the grant SFRH/BD/137277/2018.Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.publishersversionepub_ahead_of_prin

Swarm Based Implementation of a Virtual Distributed Database System in a Sensor Network

The deployment of unmanned aerial vehicles (UAVs) in recent military operations has had success in carrying out surveillance and combat missions in sensitive areas. An area of intense research on UAVs has been on controlling a group of small-sized UAVs to carry out reconnaissance missions normally undertaken by large UAVs such as Predator or Global Hawk. A control strategy for coordinating the UAV movements of such a group of UAVs adopts the bio-inspired swarm model to produce autonomous group behavior. This research proposes establishing a distributed database system on a group of swarming UAVs, providing for data storage during a reconnaissance mission. A distributed database system model is simulated treating each UAV as a distributed database site connected by a wireless network. In this model, each UAV carries a sensor and communicates to a command center when queried. Drawing equivalence to a sensor network, the network of UAVs poses as a dynamic ad-hoc sensor network. The distributed database system based on a swarm of UAVs is tested against a set of reconnaissance test suites with respect to evaluating system performance. The design of experiments focuses on the effects of varying the query input and types of swarming UAVs on overall system performance. The results show that the topology of the UAVs has a distinct impact on the output of the sensor database. The experiments measuring system delays also confirm the expectation that in a distributed system, inter-node communication costs outweigh processing costs

A Comparison of pattern classification techniques for orienting chest X-rays

The problem of orienting digital images of chest x-rays, which were captured at some multiple of 90 degrees from the true orientation, is a typical pattern classification problem. In this case, the solution to the problem must assign an instance of a digital image to one of four classes, where each class corresponds to one of the four possible orientations. A large number of techniques are available for developing a pattern classifier. Some of these techniques are characterized by independent variables whose values are difficult to relate back to the problem being solved. If a technique is highly sensitive to the values of these variables, the lack of a rigorous way of defining them can be a significant disadvantage to the inexperienced researcher. This thesis presents experiments by the author to solve the chest x-ray orientation problem using four different pattern classification techniques: genetic programming, an artificial neural network trained with back propagation, a probabilistic neural network, and a simple linear classifier. In addition, the author will demonstrate that an understanding of the design of a feature set may allow a programmer to develop a traditional program which does an adequate job of solving the classification problem. Comparisons of the different techniques will be based not only on their success at solving the problem, but also on the time required to find an acceptable solution and the degree to which each technique is sensitive to the values of the variables which characterize it. The thesis demonstrates that all of the techniques can be used to derive very accurate chest x-ray orientation classifiers. While it is dangerous to generalize the results of these experiments to pattern classification problems in general, the author will argue that the magnitude of the differences in performance between the different techniques minimizes this danger. In particular, the experiments suggest that the linear classifier is so computationally inexpensive that it is always worth trying, unless there is a priori knowledge that it will fail. The experiments also suggest that genetic programming is much more computationally expensive than are the linear classifier, artificial neural network, and probabilistic neural network techniques. Of the four conventional pattern classification techniques which were examined, it will be shown that the artificial neural network produced the most accurate classifiers for the x-ray orientation problem. In addition, the results of a number of trials suggest that the final accuracy of the classifier is relatively insensitive to the values of the parameters which characterize this technique, making it an appropriate choice for the inexperienced researcher. With respect to the ability of the resulting classifier to accurately orient sample x-rays which were not included in the training set, the artificial neural network performed well, when compared to the other techniques. Although the classifiers produced by the genetic programming technique were significantly more expensive to construct and were slightly less accurate than the best artificial neural networks, the results of genetic programming experiments can provide insights into the problem being studied, which would be difficult to discern from the classifiers produced by the other techniques. For example, one of the classifiers which was produced by genetic programming uses only eight of the twenty feature values extracted from the sample x-ray. Not only does this reduce the cost of extracting the feature values from an unknown sample, but the classifier itself would be much more efficient to evaluate than the classifiers produced by any of the other techniques

GPIS: genetic programming based image segmentation with applications to biomedical object detection

Image segmentation plays a critical role in many image analysis applications. However, it is ill-defined in nature and remains one of the most intractable problems in image processing. In this thesis, we propose a genetic programming based algorithm for image segmentation (GPIS). Typically, genetic programming is a Darwinian-evolution inspired program discovery method and in the past it has been successfully used as an automatic programming tool. We make use of this property of GP to evolve efficient and accurate image segmentation programs from a pool of basic image analysis operators. In addition, we provide no a priori information about that nature of the images to the GP. The algorithm was tested on two separate medical image databases and results show the proposed GP's ability to adapt and produce short and accurate segmentation algorithms, irrespective of the database in use. We compared our results with a popular GA based image segmentation/classification system, GENIE Pro. We found that our proposed algorithm produced accurate image segmentations performed consistently on both databases and could possibly be extended to other image databases as a general-purpose image segmentation tool

A hybrid neural network and genetic programming approach to the automatic construction of computer vision systems

Both genetic programming and neural networks are machine learning techniques that have had a wide range of success in the world of computer vision. Recently, neural networks have been able to achieve excellent results on problems that even just ten years ago would have been considered intractable, especially in the area of image classification. Additionally, genetic programming has been shown capable of evolving computer vision programs that are capable of classifying objects in images using conventional computer vision operators. While genetic algorithms have been used to evolve neural network structures and tune the hyperparameters of said networks, this thesis explores an alternative combination of these two techniques. The author asks if integrating trained neural networks with genetic programming, by framing said networks as components for a computer vision system evolver, would increase the final classification accuracy of the evolved classifier. The author also asks that if so, can such a system learn to assemble multiple simple neural networks to solve a complex problem. No claims are made to having discovered a new state of the art method for classification. Instead, the main focus of this research was to learn if it is possible to combine these two techniques in this manner. The results presented from this research indicate that such a combination does improve accuracy compared to a vision system evolved without the use of these networks