Partner: M. Bereta


Ostatnie publikacje
1.Bereta M., Burczyński T., Evolving ensembles of linear classifiers by means of clonal selection algorithm, CONTROL AND CYBERNETICS, ISSN: 0324-8569, Vol.39, No.2, pp.325-342, 2010

Streszczenie:

Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers used to construct a set of classification rules. Aggregating strategies, such as bagging and boosting, are shown to work w ell with the proposed algorithm as the base classifier.

Słowa kluczowe:

artificial immune systems, clonal selection, linear classifiers, bagging, boosting

Afiliacje autorów:

Bereta M.-other affiliation
Burczyński T.-other affiliation
20p.
2.Bereta M., Burczyński T., Immune K-means and negative selection algorithms for data analysis, INFORMATION SCIENCES, ISSN: 0020-0255, DOI: 10.1016/j.ins.2008.10.034, Vol.179, No.10, pp.1407-1425, 2009

Streszczenie:

During the last decade artificial immune systems have drawn much of the researchers’ attention. All the work that has been done allowed to develop many interesting algorithms which come in useful when solving engineering problems such as data mining and analysis, anomaly detection and many others. Being constantly developed and improved, the algorithms based on immune metaphors have some limitations, though. In this paper we elaborate on the concept of a novel artificial immune algorithm by considering the possibility of combining the clonal selection principle and the well known K-means algorithm. This novel approach and a new way of performing suppression (based on the usefulness of the evolving lymphocytes) in clonal selection result in a very effective and stable immune algorithm for both unsupervised and supervised learning. Further improvements to the cluster analysis by means of the proposed algorithm, immune K-means, are introduced. Different methods for clusters construction are compared, together with multi-point cluster validity index and a novel strategy based on minimal spanning tree (mst) and a analysis of the midpoints of the edges of the (mst). Interesting and useful improvements of the proposed approach by means of negative selection algorithms are proposed and discussed.

Słowa kluczowe:

Artificial immune systems, Clonal selection, Negative selection, Clustering, Data analysis

Afiliacje autorów:

Bereta M.-other affiliation
Burczyński T.-other affiliation
32p.