Showing papers by "Zuhair Bandar published in 2002"

Analysis of the behaviuor of a subject

Abstract: There is disclosed a method for analysing the behaviour of a subject comprising the steps of: making one or more measurements or observations of the subject; coding the measurements or observations into a plurality of channels; and analysing the channels using artificial intelligence, in order to output information relating to the psychology of the subject.

Combining multiple decision trees using fuzzy-neural inference

Abstract: This paper presents a novel approach to combining multiple decision trees, which utilizes the power of fuzzy inference techniques and a back-propagation feed forward neural network (BP-FFNN) to improve the overall classification. Crisp multiple decision trees were originally introduced to improve the performance of single decision trees, which offered a restrictive view of the domain. By combining multiple perspectives of the same domain, the information content is increased and the predictive power of the classifier is improved. A predominant weakness in creating multiple crisp trees is the generation of sharp decision boundaries at every node within all trees. The creation of fuzzy decision forests overcomes this weakness by introducing the concepts of fuzzy theory to soften the decision boundaries. To combine information from all fuzzy trees a unique combination of fuzzy inference is used to generate a series of leaf membership grades, which are then used as input to a BP-FFNN. Comparisons are made between using the fuzzy-neural approach and the use of pure fuzzy inference trees and the results indicate that considerable improvement has been made over crisp multiple decision trees.

Measuring Semantic Similarity Between Words Using Lexical Knowledge and Neural Networks

Abstract: This paper investigates the determination of semantic similarity by the incorporation of structural semantic knowledge from a lexical database and the learning ability of neural networks. The lexical database is assumed to be organised in a hierarchical structure. The extracted lexical knowledge contains the relative location of the concerned words in the lexical hierarchy. The neural network then processes available lexical knowledge to provide semantic similarity for words. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed method is effective in measuring semantic similarity between words.

Analysis of the behaviour of a subject

Abstract: There is disclosed a method for analysing the behaviour of a subject comprising the steps of: making one or more measurements or observations of the subject; coding the measurements or observations into a plurality of channels; and analysing the channels using artificial intelligence, in order to output information relating to the psychology of the subject.

Methods and apparatus for analysing the behaviour of a subject

Abstract: There is disclosed a method for analyzing the behavior of a subject comprising the steps of: making one or more measurements or observations of the subject; coding the measurements or observations into a plurality of channels; and analyzing the channels using artificial intelligence, in order to output information relating to the psychology of the subject.

Fuzzy-Neural Inference in Decision Trees

Abstract: The predominate weakness in the creation of decision trees is the strict partitions which are selected by the induction algorithm. To overcome this problem the theories of fuzzy logic have been applied to generate soft thresholds leading to the creation of fuzzy decision trees, thus allowing cases passing through the tree for classification to be assigned partial memberships down all paths. A challenging task is how these resultant membership grades are combined to produce an overall outcome. A number of theoretical fuzzy inference techniques exist, yet they have not been applied extensively in practical situations and are often domain dependent. Thus the overall classification success of the fuzzy trees has a high dependency on the optimization of the strength of the fuzzy intersection and union operators that are applied. This paper investigates a new, more general approach to combining membership grades using neural-fuzzy inference. Comparisons are made between using the fuzzy-neural approach and the use of pure fuzzy inference trees.