The prediction of bankruptcy is of significant importance with the present-day increase of bankrupt companies. In the practical applications, the cost of misclassification is worthy of consideration in the modeling in order to make accurate and desirable decisions. An effective prediction system requires the integration of the cost preference into the construction and optimization of prediction...

This contribution extends learning vector quantization to the domain of graphs. For this, we first identify graphs with points in some orbifold, then derive a generalized differentiable intrinsic metric, and finally extend the update rule of LVQ for generalized differentiable distance metrics. First experiments indicate that the proposed approach can perform comparable to state-of-the-art metho...

We propose a delineation algorithm that deals with bar-like structures of different thickness. Detection of linear structures is applicable to several fields ranging from medical images for segmentation of vessels to aerial images for delineation of roads or rivers. The proposed method is suited for any delineation problem and employs a set of BCOSFIRE filters selective for lines and line-endin...

The paper presents a design of parameters for air quality modelling and the classification of districts into classes according to their pollution. Further, it presents a model design, data pre-processing, the designs of various structures of Kohonen’s Self-organizing Feature Maps (unsupervised methods), the clustering by K-means algorithm and the classification by Learning Vector Quantization n...

Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions fro...

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers which are based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of t...

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods...

A new system for selection of reference instances, which is called the EkP system (Exactly k Prototypes), has been introduced by us recently. In this paper we study suitability of the EkP method for training data reduction on seventeen datasets. As the underlaying classifier the well known IB1 system (1-Nearest Neighbor classifier) has been chosen. We compare generalization ability of our metho...

A class-modular generalized learning vector quantization (GLVQ) ensemble method with outlier learning for handwritten digit recognition is proposed. A GLVQ classifier is one of discriminative methods. Though discriminative classifiers have remarkable ability to solve character recognition problems, they are poor at outlier resistance. To overcome this problem, a GLVQ classifier trained with bot...

Abstract. In this paper we propose a variant of the Generalized Matrix Learning Vector Quantization (GMLVQ) for dissimilarity learning on complex-valued data. Complex features can be encountered in various data domains, e.g. Fourier transformed mass spectrometry or image analysis data. Current approaches deal with complex inputs by ignoring the imaginary parts or concatenating real and imaginar...