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In order to explore the inherent law of serious collisions and provide a reference to determine the result of accidents, this paper proposed a new model of vessel collision analysis and prediction based on data mining. After collecting complete vessel collision accidents reports, indexes of the severity of vessel collision were extracted and quantified. By using the method of factor analysis, the...
This paper deals with opinion mining from unstructured textual documents. The proposed method focuses on approach with minimum preliminary requirements about the knowledge of the analysed language and thus it can be deployed to any language. The proposed method builds on artificial intelligence, which consists of Support Vector Machines classifier, Big Data analysis and genetic algorithm optimization...
This paper proposes a hybrid feature selection approach based on Genetic Algorithm (GA) to predict stock return. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an improved Genetic Algorithm (IGA) to identify an optimum feature subset and to increase the classification accuracy and scalability. We propose IGA by improving the initial...
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...
This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid...
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...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
In the wide growth of information technology, security has one challenging phase for computer and networks. Attacks on the web are increasing day-by-day. Intrusion detection system is used to detect several types of malicious attacks that can compromise the security of a computer system. Data mining techniques are used to monitor and analyze large amount of network data & classify these network...
Specific crime in the banking system is credit card fraud. Credit card usage has been increased due to the rapid growth of E-commerce techniques. Credit card fraud also increased at the same time. Prevention is better than detection. So the existing system prevented the credit card fraud by identifying fraud in the application of the Credit card. Due to the limitation of the existing system, this...
In this paper, we investigate the performance of statistical, mathematical programming and heuristic linear models for cost‐sensitive classification. In particular, we use five cost‐sensitive techniques including Fisher's discriminant analysis (DA), asymmetric misclassification cost mixed integer programming (AMC‐MIP), cost‐sensitive support vector machine (CS‐SVM), a hybrid support vector machine...
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...
This paper presents the improved algorithm for the Hybrid Approach of Neural network and Level-2 Fuzzy set (HANN-L2F). The main structure is including 2 parts. The first part is Neuro-Fuzzy system, including the MLP Neural network with the combination of the level-2 Fuzzy system. The second part is using k-nearest neighbor to classify the output from Neuro-fuzzy. The HANN-L2F is an algorithm with...
Data mining concepts have been extensively used for disease prediction in the medical field. Many Hybrid Prediction Models (HPM) have been proposed and implemented in this area, however, there is always a need for increasing accuracy and efficiency. The existing methods take into account all the features to build the classifier model thus reducing the accuracy and increasing the overall processing...
One of the key success factors of lending organizations in general and banks in particular is the assessment of borrower credit worthiness in advance during the credit evaluation process. Credit scoring models have been applied by many researchers to improve the process of assessing credit worthiness by differentiating between prospective loans on the basis of the likelihood of repayment. Thus, credit...
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...
In the present study we investigate the evolutionary feature subset selection using wrapper based genetic algorithms on Multi-temporal datasets. Feature subset selection helps in reducing the original feature dimension and also yields high performance. The evolutionary strategy attains a global optimum by reducing the computations iteratively and by traversing intelligently in the entire feature space...
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...
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...
A new algorithm of fuzzy support vector machine based on niche is presented in this paper. In this algorithm, through comparing samples niche with class niche, the method of simply using Euclidean distance to measure the relationship of samples and class in the traditional support vector machine is changed by using the minimum radius in class niche, and the disadvantages of traditional support vector...
In the Network Intrusion Detection, the large number of features increases the time and space cost, besides the irrelative redundant characteristics make the detection accuracy dropped. In order to improve detection accuracy and efficiency, a new Feature Selection method based on Rough Sets and improved Genetic Algorithms is proposed for Network Intrusion Detection. Firstly, the features are filtered...
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