One of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two...

Fuzzy Random Forests are well-known Machine Learning ensemble methods. They combine the outputs of multiple Decision Trees to improve classification performance. Moreover, they can deal with data uncertainty and imprecision thanks use fuzzy logic. Although many tasks binary, in some situations we face problem classifying into a set ordered categories. This is particular case multi-class where o...

In the paper we develop multi-class classification of Fermi-LAT gamma-ray sources using machine learning with hierarchical determination classes. One main challenges in is that size some classes relatively small, for example less than 10 associated belonging to a class. propose structure This enables us have control over and compare performance different numbers particular, class probabilities ...

Multi-class miner is well recognized method for stream data classification. For the process of multi-class miner evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of multi-class miner (MCM). For the improvement of multi-class miner particle of swarm optimization technique is used. Particle of swarm optimization controls...

We propose a new framework to construct the margin-based classifiers, in which the binary and multicategory classification problems are solved by the same principle; namely, margin-based classification via regularized empirical risk minimization. To build the framework, we propose the margin vector which is the multi-class generalization of the margin, then we further generalize the concept of ...