Decision trees and multi-level ensemble classifiers for neurological diagnostics

Cardiac autonomic neuropathy (CAN) is a well known complication of diabetes leading to impaired regulation of blood pressure and heart rate, and increases the risk of cardiac associated mortality of diabetes patients. The neurological diagnostics of CAN progression is an important problem that is being actively investigated. This paper uses data collected as part of a large and unique Diabetes Screening Complications Research Initiative (DiScRi) in Australia with data from numerous tests related to diabetes to classify CAN progression. The present paper is devoted to recent experimental investigations of the effectiveness of applications of decision trees, ensemble classifiers and multi-level ensemble classifiers for neurological diagnostics of CAN. We present the results of experiments comparing the effectiveness of ADTree, J48, NBTree, RandomTree, REPTree and SimpleCart decision tree classifiers. Our results show that SimpleCart was the most effective for the DiScRi data set in classifying CAN. We also investigated and compared the effectiveness of AdaBoost, Bagging, MultiBoost, Stacking, Decorate, Dagging, and Grading, based on Ripple Down Rules as examples of ensemble classifiers. Further, we investigated the effectiveness of these ensemble methods as a function of the base classifiers, and determined that Random Forest performed best as a base classifier, and AdaBoost, Bagging and Decorate achieved the best outcomes as meta-classifiers in this setting. Finally, we investigated the meta-classifiers that performed best in their ability to enhance the performance further within the framework of a multi-level classification paradigm. Experimental results show that the multi-level paradigm performed best when Bagging and Decorate were combined in the construction of a multi-level ensemble classifier

Performance evaluation of multi-tier ensemble classifiers for phishing websites

This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the top~ tier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi~tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi~level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature

Fusión de algoritmos bayesianos y árboles de clasificación como propuesta para la clasificación supervisada de fallos de equipos en un laboratorio de cómputos

Los algoritmos basados en redes bayesianas y árboles de decisión representan métodos que han resultado eficientes para la resolución de problemas de clasificación. Este trabajo pretende combinar estos algoritmos con el objetivo de obtener un modelo híbrido que permita aprovechar y combinar las ventajas de ambos. Con esta estrategia se pretende aumentar la precisión en los resultados de la clasificación supervisada. Este trabajo pretende detallar cual es el grado de precisión en la exactitud, cuando los algoritmos bayesianos son combinados con los árboles de decisión utilizando como recurso los métodos de fusión o ensamble Grading y Vote. Los modelos híbridos resultantes serán aplicados para la clasificación de eventos de fallos en equipos pertenecientes a un laboratorio de cómputos, con el propósito de aumentar su disponibilidad y mantenibilidad.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

GA-stacking: Evolutionary stacked generalization

Stacking is a widely used technique for combining classifiers and improving prediction accuracy. Early research in Stacking showed that selecting the right classifiers, their parameters and the meta-classifiers was a critical issue. Most of the research on this topic hand picks the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good Stacking configurations. Since this can lead to overfitting, one of the goals of this paper is to empirically evaluate the overall efficiency of the approach. A second goal is to compare our approach with the current best Stacking building techniques. The results show that our approach finds Stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to manually set up the structure of the Stacking system.This work has been partially supported by the Spanish MCyT under projects TRA2007-67374-C02-02 and TIN-2005-08818-C04. Also, it has been supported under MEC grant by TIN2005-08945-C06-05. We thank anonymous reviewers for their helpful comments.Publicad

Towards algorithm-agnostic uncertainty estimation: predicting classification error in an automated machine learning setting

Algorithms and the Foundations of Software technolog

Sistema de soporte de decisión para la gestión de fallos en equipos industriales, basado en métodos de ensamble

Los fallos en equipos industriales representan eventos críticos en el ámbito de cualquier organización. Su clasificación y caracterización representa un factor importante que apoya el proceso de toma de decisiones en las actividades de mantenimiento. La Minería de Datos ha desempeñado un rol significativo en la evaluación y clasificación de los fallos presentados. Los algoritmos basados en redes bayesianas y árboles de decisión han sido utilizados, de manera individual y en conjunto, para la construcción de modelos de clasificación híbridos, con el propósito de la evaluación y caracterización de fallos. Este trabajo propone el desarrollo de modelos híbridos usando los métodos de ensamble Grading y Vote, combinando las técnicas de redes bayesianas (BayesNet y Naive BayesUpdateable) y árboles de decisión (RandomTree). Se determina la precisión de los métodos de ensamble con los distintos algoritmos, mediante experimentos con el mismo set de datos particionado.Sociedad Argentina de Informática e Investigación Operativ