Self-Organizing Symbolic Learned Rules
نویسندگان
چکیده
In this paper we present a self-organizing process for rules obtained from a machine learning system. The resulting map can be interpreted back into the symbolic field in an attempt to make the logical representation of the original rules reflect the relationships codified by map distances. Thus, we improve the quality of the starting set of rules both in classification accuracy and in conceptual clarity.
منابع مشابه
Automatic Acquisition of Symbolic Knowledge from Subsymbolic Neural Networks
Knowledge acquisition is a bottleneck in AI applications. Neural learning is a new perspective in knowledge acquisition. In our approach we have extended Kohonen's self-organizing feature maps (SOFM) by the U-matrix method for the discovery of structures resp. classes. We have developed a machine learning algorithm, called SIG*, which automated extracts rules out of SOFM which are trained to cl...
متن کاملA Rule Extractor for Diagnosing the Type 2 Diabetes Using a Self-organizing Genetic Algorithm
Introduction: Constructing medical decision support models to automatically extract knowledge from data helps physicians in early diagnosis of disease. Interpretability of the inferential rules of these models is a key indicator in determining their performance in order to understand how they make decisions, and increase the reliability of their output. Methods: In this study, an automated hyb...
متن کاملProof of correctness for ASOCS AA3 networks
This paper analyzes adaptive algorithm 3 (AA3) of adaptive self-organizing concurrent systems (ASOCS) and proves that AA3 correctly fulfills the rules presented. Several different models for ASOCS have been developed. AA3 uses a distributed mechanism for implementing rules so correctness is not obvious. An ASOCS is an adaptive network composed of many simple computing elements operating in para...
متن کاملSelf-organizing maps and symbolic data
In data analysis new forms of complex data have to be considered like for example (symbolic data, functional data, web data, trees, SQL query and multimedia data,. . . ). In this context classical data analysis for knowledge discovery based on calculating the center of gravity can not be used because input are not Rp vectors. In this paper, we present an application on real world symbolic data ...
متن کاملRule Extraction from Self-Organizing Networks
Abstract. Generalized relevance learning vector quantization (GRLVQ) [4] constitutes a prototype based clustering algorithm based on LVQ [5] with energy function and adaptive metric. We propose a method for extracting logical rules from a trained GRLVQ-network. Real valued attributes are automatically transformed to symbolic values. The rules are given in the form of a decision tree yielding se...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1997