Search results for "Incremental decision tree"
showing 3 items of 3 documents
TREEZZY2, a Fuzzy Logic Computer Code for Fault Tree and Event Tree Analyses
2004
In conventional approach to reliability analysis using logical trees methodologies, uncertainties in system components or basic events failure probabilities are approached by assuming probability distribution functions. However, data are often insufficient for statistical estimation, and therefore it is required to resort to approximate estimations. Moreover, complicate calculations are needed to propagate uncertainties up to the final results. In our work, in order to take account of the uncertainties in system failure probabilities, the methodology based on fuzzy sets theory is used both in fault tree and event tree analyses. This paper just presents our work in this issue, which resulted…
Evolving Tree Algorithm Modifications
2007
There are many variants of the original self-organizing neural map algorithm proposed by Kohonen. One of the most recent is the Evolving Tree, a tree-shaped self-organizing network which has many interesting characteristics. This network builds a tree structure splitting the input dataset during learning. This paper presents a speed-up modification of the original training algorithm useful when the Evolving Tree network is used with complex data as images or video. After a measurement of the effectiveness an application of the modified algorithm in image segmentation is presented.
Estimating feature discriminant power in decision tree classifiers
1995
Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.