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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...
Occupational accident is a serious issue for every industry. Steel industry is considered to be one of the economic sectors having a high number of accidents. Thus, the main aim of this study is to build a model which could predict the occupational incidents (i.e., injury, near-miss, and property damage) using support vector machine (SVM) by utilizing a database comprising almost 5000 occupational...
In this study, a decision support system has been developed for land mine detection and classification. Data obtained from detector based magnetic anomaly have been used to classify the land mines. With this classification, it is decided that whether obtained data belongs to a land mine or not, and the type of mine. The meta-heuristic k-NN classifier (HKC) has been used in developed decision support...
Student performance classification is a challenging task for teacher and stakeholder for better academic planning and management. Data mining can be used to find knowledge from student data to improve the performance of classifying model. Before applying a classification model, feature selection method is proposed in data preprocessing process to find out the most significant and intrinsic features...
As increase in the internet services and usage with open access to sensitive data, necessity of security to these systems had become a need of the hour. Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more crucial issue. To detect the attacks hitting the network it is very obligatory to properly monitor the flow...
Analyses show that the absorption band position determines the type of mineral radically. The paper proposes a method of applying GA (Genetic Algorithm) to the selection of the uranium mineral band feature sub-set. First, on the fundamental of the correlation between feature-based metrics: information entropy, information gain, symmetrical uncertainty and type space, the GA which is a random search...
In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative...
We discuss an original approach to multidimensional non-stationary time series classification based on dynamic patterns analysis. The main problem in time series classification is construction of appropriate feature space. The success of classification dramatically depends on the quality of the feature space chosen. To construct this space we develop the method for extraction of dynamic patterns that...
This paper presents a Genetic Algorithm based feature selection approach for clinical decision support system, which is designed to assist physicians with decision making tasks, as to discriminate healthy people from those with appendicitis disease. We have compared the performance of Genetic Algorithm with two feature ranking algorithms namely Information Gain and Chi-Square algorithm. The genetic...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
In this paper, an evolutionary hybrid approach is studied for fault diagnosis and it is applied to classify the loopers faults in hot rolling process. The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm (GA), kernel principal component analysis (KPCA) and Least Squares Support Vector Machine (LSSVM), which can obtain better fault recognition rate. Firstly, kernel function...
This paper aims to challenge the problem of finding accurate and relevant rules for the task of classification. The scope is to improve the accuracy, or at least to provide a comparable accuracy measure, for classification algorithms implemented so far. Because the task of classification must be as accurate as possible, the paper proposes a method based on genetic algorithms to enhance the speed and...
Balancing Recall and Precision of rare class in cost-sensitive classification is a general problem. In this paper, we propose a novel cost-sensitive learning algorithm, named Adaptive Cost Optimization (AdaCO), which uses the resampling and genetic algorithm to build convex combination composite classifiers. In every base classifier's building, we use G-mean over Recall and Precision of rare class...
Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier,...
Classification is one of the tasks in data mining. Nowadays, there are many classification techniques being used to solve classification problems such as neural network, genetic algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using decision tree induction techniques. By using this approach, talent performance can be predicted...
Feature selection, structure determination and connection weights training are three key tasks for the classification problem based on neural network. Traditional feature selection methods with neural networks neglect the fact that these three tasks are interdependent and make a joint contribution to the performance of neural network, which often results in an irrational network structure and unsatisfying...
With the development and widely used of Internet and information technology, the Web has become one of the most important means to obtain information for people. According to the Web document classification and the theory of artificial neural network, a Web classification mining method based on classify support vector machine (SVM) is presented in this paper. The SVM network structure that used for...
One of the preprocessors can be used to improve the performance of brain-computer interface (BCI) systems is independent component analysis (ICA). ICA is a signal processing technique in which observed random data are transformed into components that are statistically independent from each other. This suggests the possibility of using ICA to separate different independent brain activities during motor...
A pattern classifying machine (PCM) ts-PCM for distributed data mining (DDM) was designed in this article. ts-PCM is based on a special class of linear cellular automata (CA), termed as mutiple attractor CA (MACA). Each MACA could be distributed in different sites as a base classifier. Characterization of a MACA based two stage by two linear operators of dependency string (DS) and dependency vector...
This paper presents a method of improving fuzzy k-nearest neighbor (k-NN) using genetic algorithm (GA). k-NN is an important classification algorithm. However, a major drawback of the method is that each of the patterns of known classification is considered equally important in the assignment of the pattern to be classified. This can cause difficulties in regions where pattern data overlap. To overcome...
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