An Ensemble Semi-Supervised Adaptive Resonance Theory Model with
  Explanation Capability for Pattern Classification
Farhad Pourpanah Chee Peng Lim Ali Etemad Q. M. Jonathan Wu
Most semi-supervised learning (SSL) models entail complex structures and
iterative training processes as well as face difficulties in interpreting their
predictions to users. To address these issues, this paper proposes a new
interpretable SSL model using the supervised and unsupervised Adaptive
Resonance Theory (ART) family of networks, which is denoted as SSL-ART.
Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of
prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy
ARTMAP structure to map the established prototype nodes to the target classes
using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is
devised to associate a prototype node with more than one class label. The main
advantages of SSL-ART include the capability of: (i) performing online
learning, (ii) reducing the number of redundant prototype nodes through the OtM
mapping scheme and minimizing the effects of noisy samples, and (iii) providing
an explanation facility for users to interpret the predicted outcomes. In
addition, a weighted voting strategy is introduced to form an ensemble SSL-ART
model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART,
assigns {\color{black}a different weight} to each class based on its
performance pertaining to the corresponding class. The aim is to mitigate the
effects of training data sequences on all SSL-ART members and improve the
overall performance of WESSL-ART. The experimental results on eighteen
benchmark data sets, three artificially generated data sets, and a real-world
case study indicate the benefits of the proposed SSL-ART and WESSL-ART models
for tackling pattern classification problems.