This work deals with stability in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and tree oscillations between similar concepts decrease learning speed. We introduce a heuristic to tackle this problem and an algorithm that uses this heuristic, and we provide theoretical and experimental backing to justify it. ...

This paper addresses the classification problem with imperfect data. More precisely, it extends standard decision trees to handle uncertainty in both building and classification procedures. Uncertainty here is represented by means of possibility distributions. The first part investigates the issue of building decision trees from data with uncertain class values by developing a non-specificity b...

We propose a novel decision tree based framework to detect phonetic mispronunciations produced by L2 learners caused by using inaccurate speech attributes, such as manner and place of articulation. Compared with conventional score-based CAPT (computer assisted pronunciation training) systems, our proposed framework has three advantages: (1) each mispronunciation in a tree can be interpreted and...

We consider Boolean combinations of data tree patterns as a specification and query language for XML documents. Data tree patterns are tree patterns plus variable (in)equalities which express joins between attribute values. Data tree patterns are a simple and natural formalism for expressing properties of XML documents. We consider first the model checking problem (query evaluation), we show th...

Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. These approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and t...

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer ...

In recent years tree kernels have been proposed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complexity and (b) a lower accuracy than traditional attribute/value methods. In this paper, we show that tree kernels are very helpful in the processing of natural language as (a) we provide a simple algorithm to compute tree kernels...

The concept of attributed tree transducer is a formal model for studying properties of attribute grammars In this paper for output languages of noncircular producing and visiting attributed tree transducers we introduce and prove a pumping lemma We ap ply this pumping lemma to gain two results there is no noncircular producing and visiting attributed tree transducer which computes the set of al...

The problem of deciding which subset of values of a categorical-valued attribute to branch on during decision tree generation is addressed. Algorithms such as ID3 and C4 do not address the issue and simply branch on each value of the selected attribute. The GID3* algorithm is presented and evaluated. The GID3* algorithm is a generalized version of Quinlan’s ID3 and C4, and is a non-parametric v...