An Efficient Cellular Automata-Based Classifier with Variance Decision Table

Classification is an important task of machine learning for solving a wide range of problems in conforming patterns. In the literature, machine learning algorithms dealing with non-conforming patterns are rarely proposed. In this regard, a cellular automata-based classifier (CAC) was proposed to deal with non-conforming binary patterns. Unfortunately, its ability to cope with high-dimensional and complicated problems is limited due to its applying a traditional genetic algorithm in rule ordering in CAC. Moreover, it has no mechanism to cope with ambiguous and inconsistent decision tables. Therefore, a novel proposed algorithm, called a cellular automata-based classifier with a variance decision table (CAV), was proposed to address these limitations. Firstly, we apply a novel butterfly optimization, enhanced with a mutualism scheme (m-MBOA), to manage the rule ordering in high dimensional and complicated problems. Secondly, we provide the percent coefficient of variance in creating a variance decision table, and generate a variance coefficient to estimate the best rule matrices. Thirdly, we apply a periodic boundary condition in a cellular automata (CA) boundary scheme in lieu of a null boundary condition to improve the performance of the initialized process. Empirical experiments were carried out on well-known public datasets from the OpenML repository. The experimental results show that the proposed CAV model significantly outperformed the compared CAC model and popular classification methods

Lagrangian Duality with ELM for Word Sense Multiprototype Discovery

Homonymy and polysemy are major issues in word sense disambiguation. Combining with multilayer neural network, word sense multiprototyping tackles the issues by defining multiple feature embedding representations for each word which are based on the average feature weight of the word’s different context windows called prototypes. The complexity of parameter estimation of neural network regression as well as the fixed context window size are the restrictions on the implementation of word sense multiprototyping. We propose approximating the least absolute deviation (LAD) between pair-wise word frequency covariance and pair-wise word semantic relatedness by Extreme Machine Learning (ELM) with less-constraint parameter estimation. Lagrangian duality proves the method’s feasibility. An in-cluster closeness calculation is performed to extract a variable context window to contextually identify multiprototypes of word senses based on Kmeans clustering. The higher accuracy of the discovered multiprototypes is verified by our experiments