2 research outputs found
Multi-label Classification via Adaptive Resonance Theory-based Clustering
This paper proposes a multi-label classification algorithm capable of
continual learning by applying an Adaptive Resonance Theory (ART)-based
clustering algorithm and the Bayesian approach for label probability
computation. The ART-based clustering algorithm adaptively and continually
generates prototype nodes corresponding to given data, and the generated nodes
are used as classifiers. The label probability computation independently counts
the number of label appearances for each class and calculates the Bayesian
probabilities. Thus, the label probability computation can cope with an
increase in the number of labels. Experimental results with synthetic and
real-world multi-label datasets show that the proposed algorithm has
competitive classification performance to other well-known algorithms while
realizing continual learning
Neuroengineering of Clustering Algorithms
Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv