Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several realworld databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.

. Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method - captured in what is called the ACTIVATE-EXPLAIN-FOCUS cycle - is able to utilise a rich knowledge model in producing contextdependent explanations. A specialisation of this method for each of the main subprocesses of case-based reasoning is presented, and illustrated with examples. 1 Introduction A growing part of the AI community is concerned with approaches that integrate several types of knowledge and reasoning methods (see for example [David et. al., 1993]). Although case-based reasoning is a rather new addition to the curre...

This paper describes a successful approach to concept learning for heuristic classification. Almost all current programs for this task create or use explicit, abstract generalizations. These programs are largely ineffective for domains with weak or intractable theories. An exemplar-based approach is suitable for domains with inadequate theories but raises two additional problems: determining similarity and indexing exemplars. Our approach extends the exemplar-based approach with solutions to these problems. An implementation of our approach, called Protos, has been applied to the domain of clinical audiology. After reasonable training, Protos achieved a competence level equaling that of human experts and far surpassing that of other machine learning programs. Additionally, an "ablation study" has identified the aspects of Protos that are primarily responsible for its success. 1 Introduction This paper describes a successful approach to the task of concept learning for heuristic clas...

CATO is an intelligent learning environment designed to help beginning law students learn basic skills of making arguments with cases. Using CATO, students practice tasks of induction and analogical argumentation. They practice testing theories against a body of cases and making written arguments about a problem, comparing and contrasting it to past cases. CATO’s model addresses arguments in which two opponents analogize a problem to favorable cases, distinguish unfavorable cases, assess the significance of similarities and differences between cases in light of normative knowledge about the domain, and use that knowledge to organize multi-case arguments. CATO communicates the model to students by presenting dynamically-generated argumentation examples and by reifying argument structure based on the model. CATO also provides a case database and tools based on the model that help make students ’ tasks more manageable. CATO was evaluated in the context of an actual legal writing course, in a study involving 30 first-year law students. We found that instruction with CATO leads to statistically significant improvement in students ’ basic argumentation skills, comparable

This paper describes a method for learning flexible concepts. by which are meant concepts that lack precise definition and are contextqlependent. To describe such concepts, the method employs a two-tiered represen- tation. in which the first tier captures explicitly basic concept properties, and the second tier characterizes allowable concept's modifications and context dependency. In e proposed method. the first tier, called Base Concept Representation (BCR), is created in two phases. In phase 1, the AQ-15 rule learning program is applied to induce a complete and consistent concept description from supplied examples. In phase 2, this description is optimized according to a domain-dependent quality criterion. The second tier, called the inferential concept interpretation dCI). consists of a procedure for flexible matching, and a set of inference rules. The proposed method has been implemented in the POSEIDON system. and experimentally tested on two real-world problems: [earning the concept of an acceptable umon contract, and learning voting patterns of Republicans and Democrats in the U.S. Congress. For comparison, a few other learning methods were also applied to the same problems. These methods included simple variants of exemplar-based learning, and an ID-3-tyl: decision tree learning, implemented m the ASSISTANT program. In the exl:riments, POSEIDON generated concept descriptions that were both, more accurate and also substantially simpler than those produced by the other methods.

Patdex is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, Moltke contains other parts as well, in particular a model-based approach; in Patdex where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe Patdex from a principal point of view and embed its main concepts into a theoretical framework 1 General Considerations Patdex 1 is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench 2 for technical diagnosis, which was developed at the university of Kaiserslautern over the past years (cf. e.g. [4, 5, 23]), Moltke contains other parts as well (cf. e.g. [16]), in particular a model-based approach (cf. [21, ...

We present the INRECA european project (ESPRIT 6322) on integration of induction and casebased reasoning (CBR) technologies for solving diagnostic tasks. A key distinction between casebased reasoning and induction is given in [1]: "In case-based methods, a new problem is solved by recognising its similarities to a specific known problem then transferring the solution of the known

Combining the predictions of a set of classifiers has been shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. Increased accuracy has been shown in a variety of real-world applications, ranging from protein sequence identification to determining the fat content of ground meat. Despite such individual successes, the answers are not known to fundamental questions about classifier combination, such as "Can classifiers from any given model class be combined to create a composite classifier with higher accuracy?" or "Is it possible to increase the accuracy of a given classifier by combining its predictions with those of only a small number o...

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies. 1 INTRODUCTION The past decade has seen an explosion in research on, and application of, knowledge acquisition methodologies, techniques and tools (Marcus, 1988; Boose & Gaines, 1988, 1990; Gaines & Boose, 1988; Boose, 1989). The knowledge acquisition community world-wide has grown in numbers and scope of projects. There are significant international collaborative developments involving the sharing of ideas and software. The problem ...

Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most machine learning techniques have been studied in the context of knowledge acquisition for well defined tasks, such as classification. Learning for these tasks can be handled by relatively simple algorithms. Complex domains present difficulties that can be approached by combining the strengths of several complementing learning techniques, and overcoming their weaknesses by providing alternative learning strategies. This study presents two perspectives, the macro and the micro, for viewing the issue of multistrategy learning. The macro perspective deals with the decomposition of an overall complex learning task into relatively well-defined learning tasks, and the micro perspective deals with designing multistrategy learning techniques for supporting the acquisition of knowledge for each task. The two pers...