Genetics-based Machine Learning Algorithm Research Articles

A New Hybrid Model of Fuzzy Time Series and Genetic Algorithm Based Machine Learning Algorithm: A Case Study of Forecasting Prices of Nine Types of Major Cryptocurrencies

This study pertains to the usage and effectiveness of the fuzzy time series (FTS) models and machine learning methods in forecasting movements of financial data. The datasets used in this study are the actual closing prices and transaction volumes of 9 different cryptocurrencies, from the earliest time obtainable in Yahoo! Finance, all the way to 31 Oct 2021. Firstly, this paper presents a study of the severe drawbacks of all existing literature on FTS. In particular, this article outlines severe shortcomings of all existing FTS based algorithms that caused inaccuracies among all existing FTS-based algorithms in yielding meaningful prediction. Then, a novel structure of our improvised FTS, denoted as QFTS, is presented in this paper, which aims to rectify all flaws exist in all conventional FTS based models in literature. A further hybrid of QFTS with ANN is also presented. Later, a comparative analysis of all the aforementioned FTS models is presented in terms of overall forecasting accuracy and forecasting accuracy under specific conditions. The results are being compared in terms of MAPE. The newly invented QFTS model and the QFTS-ANN hybrid is found to profoundly outperform all the existing FTS models in literature, which includes Singh's FTS model. Such innovation profoundly rectifies severe shortcomings in financial forecasting that have persisted for many years in the past literature.

Water management using genetic algorithm-based machine learning

Water scarcity is the major problem presently being faced globally; it is to be managed in an efficient manner. The water management procedure is one of the techniques to condense necessary water. The objective of water management system is that water supply agency to collect, distribute quality water without any delay and scarcity. An intelligent system is necessary for efficient production, collection and distribution. The proposed intelligent system consists of genetic operations with fitness value and neural network for training. The fitness function is used to make new intelligent members from existing population of water resources for water collection and distribution. The system is applicable for prediction about water consumption, distribution using decision-making algorithms to increase optimization performance by calculation of objective function of various population types. The regression performance of proposed intelligent system is calculated and compared with other algorithms.

Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation

We propose a parallel distributed model of a hybrid fuzzy genetics-based machine learning (GBML) algorithm to drastically decrease its computation time. Our hybrid algorithm has a Pittsburgh-style GBML framework where a rule set is coded as an individual. A Michigan-style rule-generation mechanism is used as a kind of local search. Our parallel distributed model is an island model where a population of individuals is divided into multiple islands. Training data are also divided into multiple subsets. The main feature of our model is that a different training data subset is assigned to each island. The assigned training data subsets are periodically rotated over the islands. The best rule set in each island also migrates periodically. We demonstrate through computational experiments that our model decreases the computation time of the hybrid fuzzy GBML algorithm by an order or two of magnitude using seven parallel processors without severely degrading the generalization ability of obtained fuzzy rule-based classifiers. We also examine the effects of the training data rotation and the rule set migration on the search ability of our model.

Function approximation with LWPR and XCSF: a comparative study

Function approximation, also called regression, is an important tool in numerical mathematics and engineering. The most challenging approximation problems arise, when the function class is unknown and the surface has to be approximated online from incoming samples. One way to achieve good approximations of complex non-linear functions is to cluster the input space into small patches, apply linear models in each niche, and recombine these models via a weighted sum. While it is rather simple to optimally fit a linear model to given data, it is fairly complex to find a reasonable structuring of the input space in order to exploit linearities in the underlying function. We compare two non-parametric regression algorithms that are able to approximate multi-dimensional, non-linear functions online. The XCSF Learning Classifier System is a modified version of XCS, which is a genetics-based machine learning algorithm. Locally Weighted Projection Regression is a statistics-based machine learning technique that is mainly used for function approximation tasks in robotics. For both algorithms the relevant, conflicting performance criteria are accuracy and population size, that is, the number of local models. We explore the trade-off between those criteria on three benchmark problems by means of intense grid search for Pareto optimal solutions. Detailed learning behavior is investigated using selected Pareto optimal parameters. The illustration of final input space clusterings sheds light on the structuring capabilities. A discussion of advantages and drawbacks completes this comparative study.

Tissue classification in magnetic resonance images through the hybrid approach of Michigan and Pittsburg genetic algorithm

Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin–spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable.

Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.

Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning

Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem. We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets.

Soft-computing based diagnostic tool for analyzing demyelination in magnetic resonance images

This paper proposes a soft-computing based diagnostic tool for analyzing (white matter changes) demyelination due to radiation therapy given to brain tumor cases. The tool exploits the pattern of changes in gray level distribution using a temporal sequence of magnetic resonance (MR) images. Appearance of white matter changes due to demyelination varies from patient to patient. Further, there exists inherent impreciseness in the white matter change patterns. These characteristics make use of fuzzy features well suited for describing image based temporal patterns. Correlation between these temporal patterns and actual onset of demyelination can be captured by fuzzy rules because of the inherent uncertainty associated with changes in gray level pattern in the image and occurrence of the disease. The tool is based on hybrid approach of two popular approaches of genetic algorithm based machine learning (GBML) techniques namely Michigan and Pittsburgh approach. The genetic algorithm (GA) based machine learning tool generates an optimized rule set to indicate positive (P), negative (N) or doubtful (D) cases of demyelination.

Differentiation of Proteins and Viruses Using Pyrolysis Gas Chromatography Differential Mobility Spectrometry (PY/GC/DMS) and Pattern Recognition

It is extremely important to develop novel analytical sensors for pathogen detection. Applications of such sensors range from ensuring environmental monitoring, to providing human health diagnostic measures, and to early warning systems for bioterrorism. Certain viruses and bacteria pathogens are of special concern for their ability to spread rapidly in human populations during a pandemic, and emerging variants of these pathogens are challenging to detect. This paper provides a reliable detection strategy to classify viruses from innocuous proteins. Our novel instrumentation is based on pyrolysis gas chromatography differential mobility spectrometry (PY/GC/DMS), and couples a genetic algorithm based machine learning method for pattern recognition with a microfabricated solid state biosensor. Together, this hardware and software combination allowed for an extremely high classification accuracy (94%) between the T4 bacteriophage from bovine serum albumin (BSA), an unrelated protein. This study suggests that this portable sensor can be used along with the sophisticated data mining method as a fast, reliable, and accurate tool for the recognition of viruses.

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns.

New trends in the fuzzy modeling part I: novel approaches

Nowadays, one of the most important areas of application of fuzzy set theory are fuzzy rule-based systems. These kinds of systems constitute an extension of classical rule-based systems, because they deal with “IF-THEN” rules whose antecedents and consequents are composed of fuzzy logic statements instead of classical logic. They have been successfully applied to a wide range of problems in different domains for which uncertainty and vagueness emerge in different ways. The current special issues on “New trends in fuzzy modeling (Part I: Novel approaches and Part II: Applications) collect several contributions by experts in the topic that go beyond on the design of fuzzy systems with a balance between new developments and applications. This first issue is devoted to present new trends on the design of fuzzy systems. The papers in this issue can be classified according to different paradigms and new trends in the design of fuzzy systems. We have divided them into three groups, the first one devoted to the trade-off between interpretability and precision, the second group focused on the input selection and fuzzy systems learning, and the last one including papers dealing with different new trends in the design of fuzzy systems, as the integration of boosting methods with fuzzy rules learning, a novel classification of data-driven approaches for designing fuzzy systems made on the basis of the optimisation techniques used for this purpose, and the use of default reasoning with specific rules for designing a genetics based machine learning algorithm. The first five papers in this issue belong to the first group devoted to the trade-off between interpretability and precision. In the first paper within this group, entitled “Hybrid learning models to get the interpretability-accuracy trade-off in

遺伝的機械学習アルゴリズム

遺伝的機械学習アルゴリズム