Name
Abstract | ||
|---|---|---|
In almost every area of human activity, the formation of huge databases has created a massive request for new tools to transform data into task oriented knowledge. Our work concentrates on real-world problems, where the learner has o handle problems dealing with data sets containing large amounts of irrelevant information. Our objective is to improve the way large data sets are processed. In fact, irrelevant information perturb the knowledge data discovery process. That is why we look for efficient methods to automatically analyze huge data sets and extract relevant features and examples. This paper presents an heuristic algorithm dedicated to example selection. In order to illustrate our algorithm capabilities, we present results of its application to an artificial data set, and the way it has been used to determine the best human resource allocation in a factory scheduling problem. Our experiments have indicated many advantages of the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-44491-2_11 | IDEAL |
Keywords | Field | DocType |
select learning examples,huge databases,large data set,artificial data,human resource allocation,human activity,irrelevant information,learning data,algorithm capability,heuristic algorithm,knowledge data discovery process,huge data set,new algorithm,human resource,scheduling problem | Data discovery,Data mining,Data set,Data processing,Computer science,Artificial intelligence,Knowledge representation and reasoning,Job shop scheduling,Heuristic (computer science),Algorithm,Resource allocation,Knowledge extraction,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-41450-9 | 1 | 0.37 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Chebel-Morello | 1 | 12 | 1.24 |
| E. Lereno | 2 | 1 | 0.37 |
| Pierre Baptiste | 3 | 4 | 1.81 |