This study is concerned with the design of a nonlinear transformation strategy to address the issues caused by the mismatch of marginal probability distributions between the source and target domains in transfer learning. In the process of transfer learning, the existing model (which is regarded as the available source of knowledge) is constructed in the source domain with the assumption that data points are independent and identically distributed. However, this assumption does not hold when the existing model is transferred to a new target domain. As a consequence, the performance of the existing model in the target domain deteriorates. Through mapping the original target space to a new space of the same dimensionality by using nonlinear transformations, the distributions of the data in the source and target domains could well matched each other, which significantly facilitates the transfer of accumulated knowledge to the new domain. No prior knowledge of the data distributions or the detailed information of the existing model is required. Experiments involving both synthetic dataset and real-world datasets are performed to demonstrate the effectiveness of the proposed approach in improving classification accuracy of the exiting model in the target domain.
In this paper, we introduce a new architecture of optimized Radial Basis Function neural network classifier developed with the aid of fuzzy clustering and data preprocessing techniques and discuss its comprehensive design methodology. In the preprocessing part, the Linear Discriminant Analysis (LDA) or Principal Component Analysis (PCA) algorithm forms a front end of the network. The transformed data produced here are used as the inputs of the network. In the premise part, the Fuzzy C-Means (FCM) algorithm determines the receptive field associated with the condition part of the rules. The connection weights of the classifier are of functional nature and come as polynomial functions forming the consequent part. The Particle Swarm Optimization algorithm optimizes a number of essential parameters needed to improve the accuracy of the classifier. Those optimized parameters include the type of data preprocessing, the dimensionality of the feature vectors produced by the LDA (or PCA), the number of clusters (rules), the fuzzification coefficient used in the FCM algorithm and the orders of the polynomials of networks. The performance of the proposed classifier is reported for several benchmarking data-sets and is compared with the performance of other classifiers reported in the previous studies.
We introduce and study a new concept of fuzzy computing unit. This construct is about coping with "negative" (or inhibitory) information and accommodating it in the language of fuzzy sets. The essential concept developed in this study deals with neurons and neural networks exploiting the concept of balanced fuzzy sets. We recall how the membership notion of fuzzy sets can be extended to the [-1,1] range giving rise to balanced fuzzy sets and then summarize properties of augmented (extended) logic operations on these constructs. Extended model of t-norms and t-conorms were defined and their properties were discussed in detail, several pertinent models of the logic connectives were developed and discussed in context of the concept of balanced fuzzy sets. We show that this idea is particularly appealing in neurocomputing as the "negative" information captured through balanced fuzzy sets exhibits a straightforward correspondence with inhibitory processing mechanisms encountered in neural networks. This gives rise to interesting properties of balanced fuzzy computing units and the ensuing topologies of the networks composed of such units. We study generic learning mechanisms suitable for learning in individual units. Illustrative examples concerning topologies, properties and learning of balanced fuzzy units are included.
