4,738 research outputs found
An update of the J48Consolidated WEKA’s class: CTC algorithm enhanced with the notion of coverage
This document aims to describe an update of the implementation of the J48Consolidated class within WEKA platform. The J48Consolidated class implements the CTC algorithm [2][3] which builds a unique decision tree based on a set of samples.
The J48Consolidated class extends WEKA’s J48 class which implements the well-known C4.5 algorithm. This implementation was described in the technical report "J48Consolidated: An implementation of CTC algorithm for WEKA".
The main, but not only, change in this update is the integration of the notion of coverage in order to determine the number of samples to be generated to build a consolidated tree. We define coverage as the percentage of examples of the training sample present in –or covered by– the set of generated subsamples. So, depending on the type of samples that we use, we will need more or less samples in order to achieve a specific value of coverage
J48Consolidated: an implementation of CTC algorithm for WEKA
The CTC algorithm, Consolidated Tree Construction algorithm, is a machine learning paradigm that was designed to solve a class imbalance problem, a fraud detection problem in the area of car insurance [1] where, besides, an explanation about the classification made was required. The algorithm is based on a decision tree construction algorithm, in this case the well-known C4.5, but it extracts knowledge from data using a set of samples instead of a single one as C4.5 does. In contrast to other methodologies based on several samples to build a classifier, such as bagging, the CTC builds a single tree and as a consequence, it obtains comprehensible classifiers.
The main motivation of this implementation is to make public and available an implementation of the CTC algorithm. With this purpose we have implemented the algorithm within the well-known WEKA data mining environment http://www.cs.waikato.ac.nz/ml/weka/). WEKA is an open source project that contains a collection of machine learning algorithms written in Java for data mining tasks. J48 is the implementation of C4.5 algorithm within the WEKA package. We called J48Consolidated to the implementation of CTC algorithm based on the J48 Java class
Ensemble of Example-Dependent Cost-Sensitive Decision Trees
Several real-world classification problems are example-dependent
cost-sensitive in nature, where the costs due to misclassification vary between
examples and not only within classes. However, standard classification methods
do not take these costs into account, and assume a constant cost of
misclassification errors. In previous works, some methods that take into
account the financial costs into the training of different algorithms have been
proposed, with the example-dependent cost-sensitive decision tree algorithm
being the one that gives the highest savings. In this paper we propose a new
framework of ensembles of example-dependent cost-sensitive decision-trees. The
framework consists in creating different example-dependent cost-sensitive
decision trees on random subsamples of the training set, and then combining
them using three different combination approaches. Moreover, we propose two new
cost-sensitive combination approaches; cost-sensitive weighted voting and
cost-sensitive stacking, the latter being based on the cost-sensitive logistic
regression method. Finally, using five different databases, from four
real-world applications: credit card fraud detection, churn modeling, credit
scoring and direct marketing, we evaluate the proposed method against
state-of-the-art example-dependent cost-sensitive techniques, namely,
cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision
trees. The results show that the proposed algorithms have better results for
all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio
Analysis of DNA sequence variation within marine species using Beta-coalescents
We apply recently developed inference methods based on general coalescent
processes to DNA sequence data obtained from various marine species. Several of
these species are believed to exhibit so-called shallow gene genealogies,
potentially due to extreme reproductive behaviour, e.g. via Hedgecock's
"reproduction sweepstakes". Besides the data analysis, in particular the
inference of mutation rates and the estimation of the (real) time to the most
recent common ancestor, we briefly address the question whether the genealogies
might be adequately described by so-called Beta coalescents (as opposed to
Kingman's coalescent), allowing multiple mergers of genealogies.
The choice of the underlying coalescent model for the genealogy has drastic
implications for the estimation of the above quantities, in particular the
real-time embedding of the genealogy.Comment: 15 pages, 16 figure
Contributions to comprehensible classification
xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID
PCTBagging: From inner ensembles to ensembles. A trade-off between discriminating capacity and interpretability
[EN] The use of decision trees considerably improves the discriminating capacity of ensemble classifiers. However, this process results in the classifiers no longer being interpretable, although comprehensibility is a desired trait of decision trees. Consolidation (consolidated tree construction algorithm, CTC) was introduced to improve the discriminating capacity of decision trees, whereby a set of samples is used to build the consolidated tree without sacrificing transparency. In this work, PCTBagging is presented as a hybrid approach between bagging and a consolidated tree such that part of the comprehensibility of the consolidated tree is maintained while also improving the discriminating capacity. The consolidated tree is first developed up to a certain point and then typical bagging is performed for each sample. The part of the consolidated tree to be initially developed is configured by setting a consolidation percentage. In this work, 11 different consolidation percentages are considered for PCTBagging to effectively analyse the trade-off between comprehensibility and discriminating capacity. The results of PCTBagging are compared to those of bagging, CTC and C4.5, which serves as the base for all other algorithms. PCTBagging, with a low consolidation percentage, achieves a discriminating capacity similar to that of bagging while maintaining part of the interpretable structure of the consolidated tree. PCTBagging with a consolidation percentage of 100% offers the same comprehensibility as CTC, but achieves a significantly greater discriminating capacity.This work was funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT980-16); and by the Ministry of Economy and Competitiveness of the Spanish Government and the European Regional Development Fund -ERDF (PhysComp, TIN2017-85409-P). We would also like to thank our former undergraduate student Ander Otsoa de Alda, who participated in the implementation of the PCTBagging algorithm for the WEKA platform
Contributions to comprehensible classification
xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID
A phylogenomic perspective on the radiation of ray-finned fishes based upon targeted sequencing of ultraconserved elements
Ray-finned fishes constitute the dominant radiation of vertebrates with over
30,000 species. Although molecular phylogenetics has begun to disentangle major
evolutionary relationships within this vast section of the Tree of Life, there
is no widely available approach for efficiently collecting phylogenomic data
within fishes, leaving much of the enormous potential of massively parallel
sequencing technologies for resolving major radiations in ray-finned fishes
unrealized. Here, we provide a genomic perspective on longstanding questions
regarding the diversification of major groups of ray-finned fishes through
targeted enrichment of ultraconserved nuclear DNA elements (UCEs) and their
flanking sequence. Our workflow efficiently and economically generates data
sets that are orders of magnitude larger than those produced by traditional
approaches and is well-suited to working with museum specimens. Analysis of the
UCE data set recovers a well-supported phylogeny at both shallow and deep
time-scales that supports a monophyletic relationship between Amia and
Lepisosteus (Holostei) and reveals elopomorphs and then osteoglossomorphs to be
the earliest diverging teleost lineages. Divergence time estimation based upon
14 fossil calibrations reveals that crown teleosts appeared ~270 Ma at the end
of the Permian and that elopomorphs, osteoglossomorphs, ostarioclupeomorphs,
and euteleosts diverged from one another by 205 Ma during the Triassic. Our
approach additionally reveals that sequence capture of UCE regions and their
flanking sequence offers enormous potential for resolving phylogenetic
relationships within ray-finned fishes
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