In this paper we propose a new document classification method, bridging discrepancies (so-called semantic gap) between the training set and the application sets of textual data. We demonstrate its superiority over classical text classification approaches, including traditional classifier ensembles. The method consists in combining a document categorization technique with a single classifier or ...

The paper contains the comparison between several class prediction methods (the K-Nearest Neighbour (KNN) algorithms and some variations of it) for classification of tumours using gene expression data. The KNN is a traditional classifier that uses a set of attributes for class prediction. Also are considered, the cases when these attributes (for KNN algorithm) are un-weighted (i.e. they all hav...

In this paper, we propose a new boosting algorithm for distributed databases. The main idea of the proposed method is to utilize the parallelism of the distributed databases to build an ensemble of classifiers. At each round of the algorithm, each site processes its own data locally, and calculates all needed information. A center site will collect information from all sites and build the globa...

The main idea is to formulate the tracking problem as a binary classification task and to achieve robustness by continuously updating the current classifier of the target object with respect to the current surrounding background. For this purpose we use an on-line AdaBoost feature selection algorithm [1] for tracking. The distinct advantage of the method is its capability of updating a model (c...

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen...

In this paper, a novel method for web page hierarchical classification is addressed. In our approach, SVM is used as the basic algorithm to separate any two sub-categories under the same parent node. In order to alleviate the ill shift of SVM classifier caused by imbalanced training data, we try to combine the original SVM classifier with BEV algorithm to create classifier called VOTEM. Then, a...

To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the C...

This paper initializes a two element concentration vector as a feature vector for classification and spam detection. Negative selection algorithm proposed by the immune system in solving problems in spam detection is used to distinguish spam from non-spam (self from non-self). Self concentration and non-self concentration are generated to form two element concentration vectors. In this approach...

This paper is dealt with the examination of possibilities of the algorithm of autonomous agent navigation. The ANIMAT problem was proposed to study the possibilities of ZCS classifier system learning. A ZCS classifier system is purely reactive. For this system it is essential to have a possibility to identify the global state of the environment by the input message. This shortens the applicatio...

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These "false alarms" delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank w...