Learning-based Rule-Extraction from Support Vector Machines

In recent years, support vector machines (SVMs) have shown good performance in a number of application areas, including text classification. However, the success of SVMs comes at a cost - an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important for the acceptance of this machine learning technology, especially for applications such as medical diagnosis. It is crucial for the users to understand how the system makes a decision. In this paper, a novel approach for rule-extraction from support vector machines is presented. This approach handles rule-extraction as a learning task, which proceeds in two steps. The first is to use the labeled patterns from a data set to train an SVM. The second step is to use the generated model to predict the label (class) for an extended data set or different, unlabeled data set. The resulting patterns are then used to train a decision tree learning system and to extract the corresponding rule sets. The output rule sets are verified against available knowledge for the domain problem (e.g. a medical expert), and other classification techniques, to assure correctness and validity of rules

Hybrid rule-extraction from support vector machines

Rule-extraction from artificial neural networks(ANNs) as well as support vector machines (SVMs) provide explanations for the decisions made by these systems. This explanation capability is very important in applications such as medical diagnosis. Over the last decade, a multitude of algorithms for rule-extraction from ANNs have been developed. However, rule-extraction from SVMs is not widely available yet.In this paper, a hybrid approach for rule-extraction from SVMs is outlined. This approach has two basic components: (1) data reduction using a logistic regression model and (2) learning based rule-extraction. The quality of the extracted rules is then evaluated in terms of fidelity, accuracy, consistency and comprehensibility. The rules are also verified against the available knowledge from the domain problem (diabetes) to assure correctness and validity

Learning-Based Rule-Extraction From Support Vector Machines: Performance On Benchmark Data Sets

Over the last decade, rule-extraction from neural networks (ANN) techniques have been developed to explain how classification and regression are realised by the ANN. Yet, this is not the case for support vector machines (SVMs) which also demonstrate an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important, especially for applications such as medical diagnosis. In this paper, an approach for learning-based rule-extraction from support vector machines is outlined, including an evaluation of the quality of the extracted rules in terms of fidelity, accuracy, consistency and comprehensibility. In addition, the rules are verified by use of knowledge from the problem domains as well as other classification techniques to assure correctness and validity

Knowledge Initialisation for Support Vector Machines

Since their introduction more than a decade ago, support vector machines (SVMs) have shown good performance in a number of application areas, including text classification, pattern recognition and bioinformatics. However, the success of SVMs comes at a cost - there is no way to utilise prior knowledge. SVMs are purely inductive learning machines. In this paper, a novel approach for rule initialisation for support vector machines is presented. The application domain is medical diagnosis. The approach presented here uses domain knowledge in the form of propositional rules to create a virtual data set to bias an SVM. The virtual data set is combined with real data for SVM learning. Knowledge initialisation results in better classification accuracy and enhanced rule quality compared with purely inductive learning

Eclectic rule-extraction from support vector machines

Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule- extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule- extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity

Statistical analysis of the features of diatonic music with jMusic

Much has been written about the rules of melody writing and this paper reports research that uses computer-based statistical analysis to test the efficacy of these rules. As a method to assist in the computer generation of melodies, we have devised computer software that analyses melodic features. This paper will outline the melodic features identified in melody-writing literature and the results of their fit with our statistical analysis of melodies from the western music repertoire. We will also present details of the computer-based analysis software and the jMusic software environment in which it was built. The software and jMusic environment are open source software projects that are freely available, and so opportunities to develop these tools to suit other music analysis tasks will be discussed.Hosted by the Scholarly Text and Imaging Service (SETIS), the University of Sydney Library, and the Research Institute for Humanities and Social Sciences (RIHSS), the University of Sydney

Multiagentensystem zur Wissenskommunikation in der Produktentstehung - Rapid Product Development

Der Sonderforschungsbereich (Sfb) 374 "Entwicklung und Erprobung innovativer Produkte - Rapid Prototyping" an der Universität Stuttgart thematisiert den Entwicklungsprozess von der Idee bis zum Prototyp. Im Rahmen eines multidisziplinären Ansatzes wird untersucht, inwieweit frühzeitig unterschiedliche Einflüsse auf das zu entwickelnde Produkt angewandt werden können. Durch die Nutzung schneller Iterationszyklen und der situationsgerechten Verwendung von Prototypen kann der Ansatz einer evolutionären Produktentwicklung erreicht werden. Dabei werden die Informationen der am RPD-Prozess beteiligten Arbeitsbereiche wie Kostenrechnung, Projektplanung, Konstruktion, Prototypenbau, etc. semantisch verknüpft und in einem dafür konstruierten Aktiven Semantischen Netz (ASN) abgelegt. Bei der direkten Zusammenarbeit der einzelnen RPD-Domänen entstehen zum Beispiel Abstimmungsprobleme, die Mechanismen erfordern, die nicht direkt durch die einzelnen RPD-Anwendungen oder das ASN gelöst werden können. Dafür wurde eine Multiagenten-basierte Middleware entwickelt, die Gegenstand dieses Artikels ist

Testing theories of post-error slowing

People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, an a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here, we used the drift diffusion model to isolate and identify the psychological processes responsible for post-error slowing. In a very large lexical decision data set, we found that post-error slowing was associated with an increase in response caution and—to a lesser extent—a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response-monitoring account of post-error slowing