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Utilization of machine learning algorithms in time-series data analysis is crucial to effective decision making in today's dynamic and competitive environment. One data type of growing interest is the electricity consumer load profile (LP) data. Owing to advances in the smart grid, immense amount of LP data became available to policymakers as potential to improving the electricity sector. Due to the...
In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) have been applied in conjunction with Least Square Support Vector Machine (LS-SVM) approach to optimize the total misclassification error in term of False Positive and False Negative rates...
Infertility problem is an important issue in recent decades. Semen analysis is one of the principle tasks to evaluate male partner fertility potential. It has been seen in many researches that life habits and health status affect semen quality. Data mining as a decision support system can help to recognize this effect. The artificial neural network (ANN) is a powerful data mining tool that can be...
The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. In this work we provide a new CBIR procedure which...
The goal of our data-mining multi-agent system is to facilitate data-mining experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the expertâs knowledge, the meta-learning subsystems are proposed including the parameter-space search and method recommendation based on previous experiments. In this paper...
Web information, regarded as a useful repository including abundant data and knowledge, attracts much attention from researchers and practitioners, and has been used to analyze and forecast economic and social hotspots in recent years. In this paper, a novel neural network based forecasting method is proposed for the unemployment rate prediction using search engine query data. The empirical results...
Support vector machines (SVMs) often contain a large number of support vectors which reduce the run-time speeds of decision functions. In addition, this might cause an over fitting effect where the resulting SVM adapts itself to the noise in the training set rather than the true underlying data distribution and will probably fail to correctly classify unseen examples. To obtain more fast and accurate...
Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), an acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component...
Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This...
The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
In this paper, we introduce an Intrusion Detection system (IDS) based Hybrid Evolutionary Neural Network (HENN). A brief overview of IDS, genetic algorithm, and related detection techniques are discussed. The system architecture is also introduced. Factors affecting the genetic algorithm are addressed in detail. Unlike other implementations of IDS, Input features, network structure and connection...
With the rapid development of network and communication technology in China, network marketing has begun to take its shape. In order to further improve the efficiency and quality of network marketing, it is necessary to take new technology and new method to promote the optimization of network marketing management while speeding up the development of network technology. Genetic algorithm, as an evolutional...
Fuzzy classifiers and fuzzy rules are powerful tools in data mining and knowledge discovery. In this work, intrusion detection is approached as a data mining task and genetic programming is deployed to evolve fuzzy classifiers for detection of intrusion and security problems. We train the fuzzy classifier on a data set modeled as a fuzzy information retrieval collection and investigate its ability...
Genetic Network Programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data...
Data mining is a very active and rapidly growing research area in the field of computer science. Its goal is to obtain useful knowledge for users from a database. Association rule mining from a database is one of the most well-known data mining techniques. In general, a large number of if-then rules are extracted by specifying minimum support and confidence levels. They are, however, too complicated...
The great majority of genetic programming (GP) algorithms that deal with the classification problem follow a supervised approach, i.e., they consider that all fitness cases available to evaluate their models are labeled. However, in certain application domains, a lot of human effort is required to label training data, and methods following a semi-supervised approach might be more appropriate. This...
The cancer classification through gene expression patterns becomes one of the most promising applications of the microarray technology. It is also a significant procedure in bioinformatics. In this study a grid computing based evolutionary mining approach is proposed as discriminant function for gene selection and tumor classification. The proposed approach is based on the grid computing infrastructure...
Two Computational Intelligence techniques, neural networks-based Multivariate Time Series Model Mining (MVTSMM) and Genetic Programming (GP), have been used to explore the possible relationship between solar activity and temperatures in Central England for the 1721 to 1967 period. Data driven analysis of multivariate, heterogeneous and incomplete time series are used in order to understand the extreme...
Data mining is an important process, with applications found in many business, science and industrial problems. While a wide variety of algorithms have already been proposed in the literature for classification tasks in large data sets, and the majority of them have been proven to be very effective, not all of them are flexible and easily extensible. In this paper, we introduce two new approaches...
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