Search results for "Decision Tree"

Non-invasive diagnosis in a case of bronchopulmonary sequestration and proposal of diagnostic algorhythm

2008

The case of a 43-year-old woman with intralobar pulmonary sequestration, Pryce type one, is presented. The medical history was characterised by recurrent bronchopneumonia, productive cough with purulent sputum and hemoptysis in the last three years. Diagnosis was made by CT angiography: multiplanar, maximum intensity projection and volume rendering reconstructions were visualised. A volume reduction of middle and lower lobe with multiple cyst-like bronchiectasis was detected and no evident relationship with tracheobronchial tree was pointed out. Reconstructions aimed at evaluating bronchial structures demonstrated no patency of middle and lower lobar bronchi. The study carried out after con…

Combination of osteopontin and activated leukocyte cell adhesion molecule as potent prognostic discriminators in HER2- and ER-negative breast cancer.

2010

Background: To analyse the discriminative impact of osteopontin (OPN) and activated leukocyte cell adhesion molecule (ALCAM), combined with human epidermal growth factor 2 (HER2) and oestrogen receptor (ER) in breast cancer. Methods: Osteopontin, ALCAM, HER2 and ER mRNA expression in breast cancer tissues of 481 patients were analysed (mRNA microarray analysis, kinetic RT–PCR). Hierarchical clustering was performed in training cohort A (N=100, adjuvant treatment) and validation cohorts B (N=200, no adjuvant treatment, low-risk) and C (N=181, adjuvant treatment, high-risk). Results: Negative/low ER and HER2, high OPN and low ALCAM mRNA expression helped to identify patients at particularly h…

STRATEGY TO INCREASE THE FARM COMPETITIVENESS

2014

Italy’s wine-growing production structure is highly pulverized. So, for many wine-growing farms loweri ng the production cost represents the only way of gain ing a competitive advantage. Production at average unit costs lower than competitors allows to improve prof itability. Among farming operations, winter pruning and tying of productive vine-branches require a high hu man labor. For this reason the paper presents the r esults of research conducted on a sample of Sicilian wine- producing farms in order to study the cost-effectiv eness to make the pruning and the subsequent ligation of productive branches with tools that facilitate the work. The economic analysis, after the determination o…

Sédation et analgésie en structure d’urgence (aéactualisation de la Conférence d’experts de la Sfar of 1999)

2010

A weighted distance-based approach with boosted decision trees for label ranking

2023

Label Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a set of predictor features. One of the most successful approaches to tackling the LR problem consists of using decision tree ensemble models, such as bagging, random forest, and boosting. However, these approaches, coming from the classical unweighted rank correlation measures, are not sensitive to label importance. Nevertheless, in many settings, failing to predict the ranking position of a highly relevant label should be considered more seriou…

Fall Detection Based on the Instantaneous Doppler Frequency : A Machine Learning Approach

2019

Modern societies are facing an ageing problem which comes with increased cost of healthcare. A major share of this ever-increasing cost is due to fall related injuries, which urges the development of fall detection systems. In this context, this paper paves the way for building of a radio-frequency-based fall detection system. This paper presents an activity simulator that generates the complex channel gain of indoor channels in the presence of one person performing three different activities, namely, slow fall, fast fall, and walking. We built a machine learning framework for activity recognition based on the complex channel gain. We assess the recognition accuracy of three different class…

Constructing Interpretable Classifiers to Diagnose Gastric Cancer Based on Breath Tests

2017

Quick, inexpensive and accurate diagnosis of gastric cancer is a necessity, but at this moment the available methods do not hold up. One of the most promising possibilities is breath test analysis, which is quick, relatively inexpensive and comfortable to the person tested. However, this method has not yet been well explored. Therefore in this article the authors propose using transparent classification models to explain diagnostic patterns and knowledge, which is acquired in the process. The models are induced using decision tree classification algorithms and RIPPER algorithm for decision rule induction. The accuracy of these models is compared to neural network accuracy.

Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines

2020

The Tsetlin Machine (TM) is a recent interpretable machine learning algorithm that requires relatively modest computational power, yet attains competitive accuracy in several benchmarks. TMs are inherently binary; however, many machine learning problems are continuous. While binarization of continuous data through brute-force thresholding has yielded promising accuracy, such an approach is computationally expensive and hinders extrapolation. In this paper, we address these limitations by standardizing features to support scale shifts in the transition from training data to real-world operation, typical for e.g. forecasting. For scalability, we employ sampling to reduce the number of binariz…

Efficient pruning of multilayer perceptrons using a fuzzy sigmoid activation function

2006

This Letter presents a simple and powerful pruning method for multilayer feed forward neural networks based on the fuzzy sigmoid activation function presented in [E. Soria, J. Martin, G. Camps, A. Serrano, J. Calpe, L. Gomez, A low-complexity fuzzy activation function for artificial neural networks, IEEE Trans. Neural Networks 14(6) (2003) 1576-1579]. Successful performance is obtained in standard function approximation and channel equalization problems. Pruning allows to reduce network complexity considerably, achieving a similar performance to that obtained by unpruned networks.

Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modelling

2005

Abstract This paper presents the use of artificial neural networks (ANNs) for surface ozone modelling. Due to the usual non-linear nature of problems in ecology, the use of ANNs has proven to be a common practice in this field. Nevertheless, few efforts have been made to acquire knowledge about the problems by analysing the useful, but often complex, input–output mapping performed by these models. In fact, researchers are not only interested in accurate methods but also in understandable models. In the present paper, we propose a methodology to extract the governing rules of trained ANN which, in turn, yields simplified models by using unbiased sensitivity and pruning techniques. Our propos…