Name
Abstract | ||
|---|---|---|
We present a solution to the problem of understanding a system that produces
a sequence of temporally ordered observations. Our solution is based on
generating and interpreting a set of temporal decision rules. A temporal
decision rule is a decision rule that can be used to predict or retrodict the
value of a decision attribute, using condition attributes that are observed at
times other than the decision attribute's time of observation. A rule set,
consisting of a set of temporal decision rules with the same decision
attribute, can be interpreted by our Temporal Investigation Method for
Enregistered Record Sequences (TIMERS) to signify an instantaneous, an acausal
or a possibly causal relationship between the condition attributes and the
decision attribute. We show the effectiveness of our method, by describing a
number of experiments with both synthetic and real temporal data. |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | temporal data,decision rule |
Field | DocType | Volume |
Decision rule,Decision tree,Data mining,Partially observable Markov decision process,Computer science,Temporal database,Weighted sum model,Influence diagram,Artificial intelligence,Evidential reasoning approach,Evidential decision theory,Machine learning | Journal | abs/1004.3 |
Citations | PageRank | References |
1 | 0.40 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamran Karimi | 1 | 118 | 17.23 |
| Howard J. Hamilton | 2 | 1501 | 145.55 |