International Journal of Approximate Reasoning
Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
A high performance k-NN classifier using a binary correlation matrix memory
Proceedings of the 1998 conference on Advances in neural information processing systems II
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
A PCI Bus Based Correlation Matrix Memory and Its Application to k-NN Classification
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
A high performance k-NN approach using binary neural networks
Neural Networks
A binary neural k-nearest neighbour technique
Knowledge and Information Systems
Evolutionary multiobjective optimization and multiobjective fuzzy system design
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
A fuzzy classifier based on correlation matrix memories
FS'09 Proceedings of the 10th WSEAS international conference on Fuzzy systems
Improved AURA k-Nearest Neighbour Approach
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Fuzzy modeling using generalized neural networks and Kalman filter algorithm
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On fuzzy associative memory with multiple-rule storage capacity
IEEE Transactions on Fuzzy Systems
Implicative Fuzzy Associative Memories
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.01 |
Classification is probably the most frequently encountered problem in machine learning (ML). The most successful ML techniques like multi-layer perceptrons or support vector machines constitute very complex systems and the underlying reasoning processes of a classification decision are most often incomprehensible. We propose a classification system based on a hybridization of binary correlation matrix memories and fuzzy logic that yields interpretable solutions to classification tasks. A binary correlation matrix memory is a simple single-layered network consisting of a matrix with binary weights with easy to understand dynamics. Fuzzy logic has proven to be a suitable framework for reasoning under uncertainty and modelling human language concepts. The usage of binary correlation matrix memories and of fuzzy logic facilitates interpretability. Two fuzzy recall algorithms carry out the classification. The first one resembles fuzzy inference, uses fuzzy operators, and can directly be translated into a fuzzy ruleset in human language. The second recall algorithm is based on a well known classification technique, that is fuzzy K-nearest neighbour classification. The proposed classifier is benchmarked on six different data sets and compared to other systems, that is, a multi-layer perceptron, a support vector machine, an adaptive neuro-fuzzy inference system, and fuzzy and standard K-nearest neighbour classification. Besides its advantage of being interpretable, the proposed system shows strong performance on most of the data sets.