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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Towards Intelligent Assistance for a Data Mining Process: An Ontology-based Approach for Cost-sensitive Classification |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Journal Title | IEEE Transactions on Knowledge and Data Engineering |
| Geographical Reach | international |
| Volume | 17 |
| Number | 4 |
| Page Range | 503 - 518 |
| Date | 2005 |
| Abstract Text | A data mining (DM) process involves multiple stages. A simple, but typical, process might in-clude preprocessing data, applying a data-mining algorithm, and postprocessing the mining re-sults. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. Extending notions developed for statistical expert systems we present a prototype Intelligent Discovery Assistant (IDA), which provides users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to show that an IDA can indeed provide useful enumerations and effective rankings in the context of simple classification processes. We discuss how an IDA could be an important tool for knowledge sharing among a team of data miners. Finally, we illustrate the claims with a com-prehensive demonstration of cost-sensitive classification using a more involved process and data from the 1998 KDDCUP competition. |
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