Interpretable boosted-decision-tree analysis for the Majorana Demonstrator

Boosted Decision Tree for Q-matrix Refinement

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Qmatrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both t...

Interpretable Boosted Naïve Bayes Classification

Voting methods such as boosting and bagging provide substantial improvements in classification performance in many problem domains. However, the resulting predictions can prove inscrutable to end-users. This is especially problematic in domains such as medicine, where end-user acceptance often depends on the ability of a classifier to explain its reasoning. Here we propose a variant of the boos...

A Binary Decision Tree Implementation of a Boosted Strong Classifier

Viola and Jones [1] proposed the influential rapid object detection algorithm. They used AdaBoost to select from a large pool a set of simple features and constructed a strong classifier of the form {j αjhj(x) ≥ θ} where each hj(x) is a binary weak classifier based on a simple feature. In this paper, we construct, using statistical detection theory, a binary decision tree from the strong classi...

Tree Structured Interpretable Regression

An interpretable classifier for detection of cardiac arrhythmias by using the fuzzy decision tree

An extraction of medical knowledge from cardiological data is proposed in this work, it is based on relevant intelligent method called fuzzy decision tree. It could lead to increase understanding the cause of various abnormal beats in cardiac activity, leading to a better medical diagnosis. The performance of this technique is evaluated on the MIT-BIH Arrhythmia Database following the AAMI reco...