Imbalanced data sets have significantly unequal distributions between classes. This between-class imbalance causes conventional classification methods to favor majority classes, resulting in very low or even no detection of minority classes. A Min-Max modular support vector machine (M-SVM) approaches this problem by decomposing the training input sets of the majority classes into subsets of sim...

Classification is the grouping of objects according to their characteristics. Scientists use classification system to organize information into logically related chunks so that it will be easy to analyze and evaluate. There are various classification techniques ranging from simple techniques such as Rule based and Nearest-Neighbour classifiers to more advanced techniques such as Support Vector ...

This paper presents a scheme to identify the blur parameters using support vector machine (SVM) Multiclass approach has been used to classify the length of motion blur and sigma parameter of atmospheric blur. Different models of SVM have been constructed to classify the parameters. Experimental results show the robustness of the proposed approach to classify blur parameters.

In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our ...

We propose a method based on the support vector machine to assess a set of clinical parameters for MCG diagnoses. By using this method, we can conclude whether the parameters are suitable for classifying a specific disease, and we can extract the dominantly working parameters from the set. In this study, we test a well-known set of the field-map parameters for ischemia detection by using our me...

Support vector machine has been explored and many applications found within various research areas and application domains. Many support vector machine techniques have been specifically developed for certain application domains. This paper is an attempt to provide an overview on applications of support vector machines within the oil refineries to the professionals inside oil refineries, researc...

The knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that ...

We propose a novel online monitoring approach to distinguish between attacks and normal activity in SIP-based Voice over IP environments. We demonstrate the e ciency of the approach even when only limited data sets are used in learning phase. The solution builds on the monitoring of a set of 38 features in VoIP ows and uses Support Vector Machines for classi cation. We validate our proposal thr...

Article history: Received 20 August 2007 Accepted 27 February 2008 Available online 15 May 2008