Many real data sets are imbalanced and contain a large number of a certain type of patterns, but a very small number of another type of patterns. Normal classification methods, such as support vector machine (SVM), do not work well for these imbalanced data sets (IDS). It is difficult for SVMs to get the optimal separation hyperplane when they are trained with imbalanced data. In this paper, we propose a genetic algorithm (GA)-based classification method. A draft hyperplane and support vectors are first generated by SVMs. Then, GA is applied to compensate the imbalanced data. Finally, SVM is used again to find the best hyperplane from the generated data points. Compared with the other popular classification algorithms, our method has better classification accuracy for several IDS.

Many real data sets are imbalanced, which contain a large number of certain type objects and a very small number of opposite type objects. Normal classification methods, such as support vector machine (SVM), do not work well for these skewed data sets. In this paper we propose a genetic algorithm (GA) based classification method. We first use SVM to generate a draft hyper plane and support vectors. Then GA is applied to find new data points in the sensible region or classification margin. Finally, SVM is used again to find the best hyper plane from the generated data points. Compared with the other popular classification algorithms, the proposed method has better classification accuracy for several skewed data sets.

Support Vector Machine (SVM) is an important classification method used in a many areas. The training of SVM is almost O(n^{2}) in time and space. Some methods to reduce the training complexity have been proposed in last years. Data selection methods for SVM select most important examples from training data sets to improve its training time. This paper introduces a novel data reduction method that works detecting clusters and then selects some examples from them. Different from other state of the art algorithms, the novel method uses a decision tree to form partitions that are treated as clusters, and then executes a guided random selection of examples. The clusters discovered by a decision tree can be linearly separable, taking advantage of the Eidelheit separation theorem, it is possible to reduce the size of training sets by carefully selecting examples from training sets. The novel method was compared with LibSVM using public available data sets, experiments demonstrate an important reduction of the size of training sets whereas showing only a slight decreasing in the accuracy of classifier.

In recent years, vertical partitioning techniques have been employed in multimedia databases to achieve efficient retrieval of multimedia objects. These techniques are static because the input to the partitioning process, which includes queries accessing database and their frequency as well as the database schema, is obtained from an earlier analysis stage. This implies that when the system undergoes sufficient changes, a new analysis stage is carried out to re-run the partitioning process. Multimedia databases are accessed by many users simultaneously, therefore queries and their frequency tend to quickly change over time. In this context, dynamic vertical partitioning can significantly improve performance. In this paper we present an active system called DYMOND (DYnamic Multimedia ON line Distribution), which performs a dynamic vertical partitioning in multimedia databases to improve query performance. Experimental results on benchmark multimedia databases clarify the validness of our system.

Support Vector Machines (SVMs) are high-accuracy classifiers. However, normal SVM algorithms are unsuitable for classification of large data sets because of their training complexity. In this paper, we propose a novel SVM classification approach for

Support vector machines (SVMs) are known to be excellent algorithms for classification problems. The principal disadvantage of SVMs is due to its excessive training time in large data set, such as DNA sequences. This paper presents a novel SVMs classification method which reduces significantly the input data set using Bayesian technique. Using this system, we are able to predict with a high accuracy huge data sets in a reasonable time. The system has been tested successfully on large splice-junction gene sequences (DNA). Experimental results show that the accuracy obtained by the proposed algorithm is comparable (98.2) with other SVMs implementations such as SMO (98.4%), LibSVM (98.4%), and Simple SVM (97.6%). Furthermore the proposed approach is scalable to large data sets with high classification accuracy.

In recent years support vector machines (SVM) has received considerable attention due to its high generalization ability and performance for a wide range of applications. However, the most important problem of this method is slow training for classification problems with a large data sets because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. This paper presents a novel SVM classification approach for large data sets by reducing training data and train the support vector machine using only these data. In this algorithm, a first stage uses SVM classification on a small data set in order to gets a sketch of classes distribution and labels the support vectors as a data set with label +1 and the other points as a data set with label -1. We call this change of classes. Then the algorithm obtains the classification hyperplane and classify the original input data set, the data points obtained with label +1 constitute the data points in the boundary of each original class and represent the most important data points, these data points are used as training data for a posterior SVM classification. The effectiveness of the approach proposed is supported by experimental results.

Despite of good theoretic foundations and high classification accuracy of support vector machine (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel two stages SVM classification approach for large data sets by randomly selecting training data. The first stage SVM classification gets a sketch of support vector distribution. Then the neighbors of these support vectors in original data set are used as training data for the second stage SVM classification. Experimental results demonstrate that our approach have good classification accuracy while the training is significantly faster than other SVM classifiers.