Induction of decision trees using fuzzy partitions

Induction of decision trees using RELIEFF

In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between them. Greedy search prevents current inductive machine learning algorithms to detect signiicant dependencies between the attributes. Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that...

Induction of Decision Trees

Fuzzy decision trees

Decision trees are arguably one of the most popular choices for learning and reasoning systems, especially when it comes to learning from discrete valued (feature based) examples. Because of the success they acquired in their area, there have been many attempts to generalize the method to better suit working with real-valued, numerical attributes, but also for missing values or even numerical o...

Learning fuzzy decision trees

We present a recurrent neural network which learns to suggest the next move during the descent along the branches of a decision tree. More precisely, given a decision instance represented by a node in the decision tree, the network provides the degree of membership of each possible move to the fuzzy set z.Lt;good movez.Gt;. These fuzzy values constitute the core of the probability of selecting ...

Incremental Fuzzy Decision Trees

We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification and generalization of data; it utilizes fuzzy logic, that provides for a fine grained description of classified items adequate for human reasoning; and it is incremental, allowing rapid alternation of classification and learning ...