Due to the non-linear but highly interpretable representations, decision tree (DT) models have significantly attracted a lot of attention of researchers. However, it is difficult to understand and interpret DT models in ultrahigh dimensions and DT models usually suffer from the curse of dimensionality and achieve degenerated performance when there are many noisy features. To address these issue...

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...

A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from graph-structured data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search. Meanwhile, a decision tree is an effective means of data classification from which rules that are easy to understand can be obtained. However, a decision tree could not ...

Decision tree (DT) is one of the most popular approaches for machine learning. Using DTs, we can extract comprehensible decision rules, and make decisions based only on useful features. The drawback is that, once a DT is designed, there is no free parameter for further development. On the contrary, a neural network (NN) is adaptable or learnable, but the number of free parameters is usually too...

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...

This study presents an approach to speaker clustering using adaptive decision tree-based phone cluster models (DT-PCMs). First, a large broadcast news database is used to train a set of phone models for universal speakers. The multi-space probability distributed-hidden Markov model (MSD-HMM) is adopted for phone modeling. Confusing phone models are merged into phone clusters. Next, for each sta...

Decision tree is a data mining technique used for the classification and forecasting of the data. It is the supervised learning algorithm that follows the greedy approach and works in a top down manner. Decision tree uses white box model approach and classifies the data in a hierarchical structure. It makes data easy to represent and understand. It can handle a large database and works well wit...