A Rule Extraction Algorithm That Scales Between Fidelity and Comprehensibility

Sonai Muthu Anbananthen, Kalaiarasi; Chan Huan Pheng, Fabian; Subramaniam, Subhacini; Sayeed, Shohel; Eldin Abdu Ali Abusham, Eimad
August 2012
Asian Journal of Scientific Research;2012, Vol. 5 Issue 3, p121
Academic Journal
Fidelity and comprehensibility are the common measures used in the evaluation of rules extracted from neural networks. However, these two measures are found to be inverse relations of one another. Since the needs of comprehensibility or fidelity may vary depending on the user or application, this paper presented a significance based rule extraction algorithm that allows a user set parameter to scale between the desired degree of fidelity and comprehensibility of the rules extracted. A detailed explanation and example application of this algorithm is presented as well as experimental results on several neural network ensembles.


Related Articles

  • A Hybrid Method: MCSA-CNN for Image Noise Cancellation. Wen-Pin Tsai; Yi-Hui Su; Chiao-Yu Chuang; Te-Jen Su // International MultiConference of Engineers & Computer Scientists;2007, p65 

    In this paper, a new method for image noise cancellation by designing the templates of cellular neural network (CNN) is introduced. We propose a modified clonal selection algorithm (MCSA) which has an adaptive maturation strategy based on affinity and clone framework to search approximate...

  • ERANN: An Algorithm to Extract Symbolic Rules from Trained Artificial Neural Networks. Kamruzzaman, S. M.; Hamid, Abdul; Jehad Sarkar, A. M. // IETE Journal of Research;May/Apr2012, Vol. 58 Issue 2, p138 

    This paper presents an algorithm to extract symbolic rules from trained artificial neural networks (ANNs), called ERANN. In many applications, it is desirable to extract knowledge from ANNs for the users to gain a better understanding of how the networks solve the problems. Although ANN usually...

  • Basic Competitive Neural Networks as Adaptive Mechanisms for Non-Stationary Colour Quantisation. Gonzalez, A. I.; Graña, M.; Cottrell, M. // Neural Computing & Applications;1999, Vol. 8 Issue 4 

    In this paper we consider the application of two basic Competitive Neural Networks (CNN) to the adaptive computation of colour representatives on image sequences that show non-stationary distributions of pixel colours. The tested algorithms are the Simple Competitive Learning (SCL) algorithm and...

  • A generalised regression algorithm for Web page categorisation. Anagnostopoulos, Ioannis; Anagnostopoulos, Christos; Kouzas, George; Vergados, Dimitrios // Neural Computing & Applications;2004, Vol. 13 Issue 3, p229 

    This paper proposes an information system that classifies Web pages according a taxonomy, which is mainly used from seven search engines/directories. The proposed classifier is a four-layer generalised regression neural network (GRNN) that aims to perform the information segmentation according...

  • Neural network modelling of word production in Finnish: coding semantic and non-semantic features. Järvelin, Antti; Juhola, Martti; Laine, Matti // Neural Computing & Applications;2006, Vol. 15 Issue 2, p91 

    The objective of our research is to computationally model word production and its disorders by means of artificial neural networks. In the current study we develop and analyze an algorithm that generates a distributed semantic coding from a given semantic tree-structure classification of words....

  • A neural networks-based negative selection algorithm in fault diagnosis. Gao, X. Z.; Ovaska, S. J.; Wang, X.; Chow, M. Y. // Neural Computing & Applications;2008, Vol. 17 Issue 1, p91 

    Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self...

  • The application of ridge polynomial neural network to multi-step ahead financial time series prediction. Ghazali, R.; Hussain, A. J.; Liatsis, P.; Tawfik, H. // Neural Computing & Applications;2008, Vol. 17 Issue 3, p311 

    Motivated by the slow learning properties of multilayer perceptrons (MLPs) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN), which...

  • A novel approach to color normalization using neural network. Cheng, H. D.; Xiaopeng Cai; Rui Min // Neural Computing & Applications;2009, Vol. 18 Issue 3, p237 

    Color is a powerful descriptor that often simplifies object extraction and identification, and many computer vision systems use color to aid object recognition. However, image colors strongly depend on lighting geometry (direction and intensity of light source) and illuminant color (spectral...

  • Is artificial intelligence for real? Woelfel, Joseph // Marketing Tools;May96, Vol. 3 Issue 3, p63 

    Discusses various aspects of artificial intelligence. Elements of an expert systems; Main criticisms of expert systems; Neural networks; Genetic algorithms; Artificial intelligence in the real world.


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics