Abstract: Workshop on Nonstationary Models of Pattern Recognition and Classifier Combinations

Abstract: We live in the era of massive data processing. Computational requirements on information processing and retrieval systems are therefore enormous - not only huge amounts of data needs to be processed and classified but also the systems need to deal with data multidimensionality. However, only recently data processing methods were extended to directly deal with multidimensional N-D patterns, without their prior vectorization, thanks to application of tensors. This talk will be focused on computational aspects of data processing and pattern recognition with tensors. We will present a systematic overview of tensor algebra and tensor decomposition methods with special stress on their applications in data representation, analysis, as well as pattern recognition. In the talk we will especially emphasize practical aspects, as well as implementation issues, of the presented algorithms. Prof. Cyganek bio: Bogusław Cyganek received his M.Sc. degree in electronics in 1993, and then M.Sc. in computer science in 1996, from the AGH University of Science and Technology, Krakow, Poland. He obtained his Ph.D. degree cum laude in 2001 with a thesis on correlation of stereo images, and D.Sc. degree in 2011 with a thesis on methods and algorithms of object recognition in digital images.
 During recent years dr. Bogusław Cyganek cooperated with many scientific and industrial partners such as Glasgow University Scotland UK, DLR Germany, and Surrey University UK, as well as Nisus Writer, USA, Compression Techniques, USA, Pandora Int., UK, and The Polished Group, Poland. He is an associated professor at the Department of Electronics of the AGH University of Science and Technology, Poland, currently serving as a visiting professor to the Wroclaw Technical University in the ENGINE project. His research interests include computer vision, pattern recognition, data mining, as well as development of embedded systems. He is an author or a co-author of over a hundred of conference and journal papers, as well as books with the latest “Object Detection and Recognition in Digital Images: Theory and Practice” published by Wiley in 2013. Dr. Cyganek is a senior member of the IEEE and member of IAPR and SPIE.

Abstract: This paper introduces an improvement on the recently published Hybrid Extreme Rotation Forest (HERF), consisting in the anticipative determination of the the fraction of each classifier architecture included in the ensemble. We call it AHERF. Both HERF and AHERF are hetero- geneous classifier ensembles, which aim to profit from the diverse problem domain specificities of each classifier architecture in order to achieve improved generalization over a larger spec- trum of problem domains. In this paper AHERF are built from a pool of Decision Trees (DT), Extreme Learning Machines (ELM), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Adaboost, Random Forests (RF), and Gaussian Naive Bayes (GNB) classifiers. Given a problem dataset, the process of anticipative determination of the ensemble composition is as follows: First, we estimate the performance of each classifier architecture by independent pilot cross-validation experiments on a small subsample of the data. Next, classifier architectures are ranked according to their accuracy results. A probability distribution of classifier architec- tures appearing in the ensemble is built from this ranking. Finally, the type of each individual classifier is decided by sampling this probability distribution. Computational experiments on a collection of benchmark classification problems shows improvement on the original HERF, and other state-of-the-art approaches.

Abstract: This paper address the data mining task of classifying data stream with concept drift. The proposed algorithm, named Concept-adapting Evolutionary Algorithm For Decision Tree (CEVOT) does not require any knowledge of the environment in which it operates (e.g. numbers and rates of drifts). The novelty of the approach is combining tree learner and evolutionary algorithm, where the decision tree is learned incrementally and all information (knowledge) are stored in the internal structure of the trees’ population. The proposed algorithm is experimentally compared with state-of-the-art stream methods on several real live and synthetic datasets. Results proves its high performance in term of accuracy and processing time.