Most decision-tree induction algorithms are using a local greedy strategy, where a leaf is always split on the best attribute according to a given attribute selection criterion. A more accurate model could possibly be found by looking ahead for alternative subtrees. However, some researchers argue that the look-ahead should not be used due to a negative effect (called ―decision tree pathology‖)...

We present a new algorithm that follows “divide and conquer” machine learning approach and exhibits a few interesting cognitive properties. The algorithm aims at building the decision tree with only one terminal node per class. Splits of tree nodes are constrained to functions that take identical values (true or false) for every instance within the same class. Appropriate splits are found throu...

A decision tree is an effective means of data classification from which one can obtain rules that are easy to understand. However, decision trees cannot be conventionally constructed for data which are not explicitly expressed with attribute-value pairs such as graph-structured data. We have proposed a novel algorithm, named Chunkingless Graph-Based Induction (Cl-GBI), for extracting typical pa...

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

Ulilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects or interest Itself but also neighbours of the objects in order to extract useful and Interesting patterns. One or classilication algorithms namely the 103 algorithm which originally designed for a non-sp...

We present a decision-tree-based symbolic rule induction system for categorizing text documents automatically. Our method for rule induction involves the novel combination of (1) a fast decision tree induction algorithm especially suited to text data and (2) a new method for converting a decision tree to a rule set that is simplified, but still logically equivalent to, the original tree. We rep...

significant advantages of intermodal and containerized transport have increased the global interest to this mode of transportation. this growing interest is reflected in the annual volume of container cargo growth. however, the container transport inside iran does not have a proper place. comparing the count of containers entering and leaving ports with the statistics obtained from railway and ...

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common s...

There are many methods to prune decision trees, but the idea of cost-sensitive pruning has received much less investigation even though additional flexibility and increased performance can be obtained from this method. In this paper, we introduce a cost-sensitive decision tree pruning algorithm called CC4.5 based on the C4.5 algorithm. This algorithm uses the same method as C4.5 to construct th...

We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their p...