This paper compares the performance of artificial neural networks and boosted decision trees, with and without cascade training, for tagging b-jets in a collider experiment. It is shown, using a Monte Carlo simulation of WH → lνqq̄ events, that boosted decision trees outperform artificial neural networks. Furthermore, cascade training can substantially improve the performance of both boosted dec...

Decision trees have been well studied and widely used in knowledge discovery and decision support systems. Although the problem of finding an optimal decision tree has received attention, it is a hard optimization problem. Here we propose utilizing a genetic algorithm to improve on the finding of ap-propriate decision trees. We present a method to encode/decode a decision tree to/from a chromo-...

OBJECTIVE The purpose of this article is to compare the diagnostic accuracy of induced decision trees with that of pruned neural networks and to improve the accuracy and interpretation of breast cancer diagnosis from readings of thin-needle aspirate by identifying cases likely to be misclassified by induced decision rules. METHOD Using an online database consisting of 699 cases of suspected b...

OBJECTIVES The aims of this work were: to define an abstract notation for interactive decision trees; to formally analyse exploration errors in such trees through automated translation to Lotos (language of temporal ordering specification); to generate tree implementations through automated translation for an existing tree viewer, and to demonstrate the approach on healthcare examples created b...

Many organisations, nowadays, have developed their own databases, in which a large amount of valuable information, e.g., customers’ personal profiles, is stored. Such information plays an important role in organisations’ development processes as it can help them gain a better understanding of customers’ needs. To effectively extract such information and identify hidden relationships, there is a...

As a classiier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. We empirically characterize domains in which SE-trees are particularly advantageous relative to decision trees. Speciically, we show that: 1. SE-trees excel in domains in which relatively few examples are available; and 2. SE-trees excel in noisy domains. In noisy domains, we discover that SE-trees ...

This paper presents a novel Fourier analysis-based technique to aggregate, communicate, and visualize decision trees in a mobile environment. Fourier representation of a decision tree has several useful properties that are particularly useful for mining continuous data streams from small mobile computing devices. This paper presents algorithms to compute the Fourier spectrum of a decision tree ...

We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultaneously, by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost environment with Multi-Task Decision Trees as weak classifiers. We first adapt the well known decision tree learning to the multi-task ...

Top-down induction of decison trees (TDIDT) is a very popular machine learning technique. Up till now, it has mainly used for propositional learning, but seldomly for relational learning or inductive logic programming. The main contribution of this paper is the introduction of logic decision trees, which make it possible to use TDIDT in inductive logic programming. An implementation of this top...

Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The \optimal" decision tree is characterized, and both a primal and dual space formulation for constructing the tree are proposed. The result is a method for generating logically simple decision trees with multivariate linear o...