Abstract—The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC)...

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools have been currently developed. Intrusion Detection Systems aim at detecting intruders who elude “first line” protection. In this paper, a pattern recognition approach to network intrusion detection based on the fusion of mult...

There have been many recent efforts in contentbased retrieval to perform automatic classification of images/visual objects. Most approaches, however, have focused on using individual classifiers. In this paper, we study the way in which, in a dynamic framework, multiple classifiers can be combined when applying Visual Object Detectors. We propose a hybrid classifier combination approach, in whi...

The generalization abilities of machine learning algorithms often depend on the algorithms' initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fu...

In TRECVID 2007 high-level feature (HLF) detection, we extend the well-known LIBSVM and develop a toolkit specifically for HLF detection. The package shortens the learning time and provides a framework for researchers to easily conduct experiments. We efficiently and effectively aggregate detectors of training past data to achieve better performances. We propose post-processing techniques, conc...

Profound theoretical analysis is performed of small-sample properties of trainable fusion rules to determine in which situations neural network ensembles can improve or degrade classification results. We consider small sample effects, specific only to multiple classifiers system design in the two-category case of two important fusion rules: (1) linear weighted average (weighted voting), realize...

combining classifiers appears as a natural step forward when a critical mass of knowledge of single classifier models has been accumulated. Although there are many unanswered questions about matching classifiers to real-life problems, combining classifiers is rapidly growing and enjoying a lot of attention from pattern recognition and machine learning communities. For any pattern classification...

This paper describes a method for fusing a collection of classifiers where the fusion can compensate for some positive correlation among the classifiers. Specifically, it does not require the assumption of evidential independence of the classifiers to be fused (such as Dempster Shafer’s fusion rule). The proposed method is associative, which allows fusing three or more classifiers irrespective ...

In practical biometric verification applications, we expect to observe a large variability of biometric data. Single classifiers have insufficient accuracy in such cases. Fusion of multiple classifiers is proposed to improve accuracy. Typically, classifier decisions are fused using a decision fusion rule. Usually, research is done on finding the best decision fusion rule, given the set of class...

Constructing a precise classifier is an important issue in pattern recognition task. Combination the decision of several competing classifiers to achieve improved classification accuracy has become interested in many research areas. In this study, Artificial Immune System (AIS) as an effective artificial intelligence technique was used for designing of several efficient classifiers. Combination...