Empirical evaluation of optimized stacking configurations

Proceeding of: 16th IEEE International Conference on Tools with Artificial Intelligence, 15-17 Nov. 2004, Boca Ratón, FloridaStacking is one of the most used techniques for combining classifiers and improves prediction accuracy. Early research in stacking showed that selecting the right classifiers, their parameters and the metaclassifiers was the main bottleneck for its use. Most of the research on this topic selects by hand 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 work is to evaluate empirically the overall efficiency of the approach. A second goal is to compare our approach with 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 set up manually the structure of the stacking system.Publicad

Various Approaches of Support vector Machines and combined Classifiers in Face Recognition

In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition

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

Un nuevo conjunto de datos para la detección de roya en cultivos de café Colombianos basado en clasificadores

Coffee production is the main agricultural activity in Colombia. More than 350.000 Colombian families depend on coffee harvest. Since coffee rust disease was first reported in the country in 1983, these families have had to face severe consequences. Recently, machine learning approaches have built a dataset for monitoring coffee rust incidence that involves weather conditions and physic crop properties. This background encouraged us to build a dataset for coffee rust detection in Colombian crops through data mining process as Cross Industry Standard Process for Data Mining (CRISP-DM). In this paper we define a proper data to generate accurate models; once the dataset is built, this is tested using classifiers as: Support Vector Regression, Backpropagation Neural Networks and Regression Trees.La producción de café es la principal actividad agrícola en Colombia. Más de 350.000 familias colombianas dependen de la cosecha de café. En este sentido, la roya fue reportada por primera vez en el país en 1983, y desde entonces estas familias han tenido que enfrentar graves consecuencias. Recientemente, diversos enfoques basados en aprendizaje automático han construido un conjunto de datos para el monitoreo de la incidencia de la roya del café, teniendo en cuenta las condiciones climáticas y las propiedades físicas de los cultivos. Estas investigaciones motivaron la creación de un conjunto de datos para la detección de la roya en cultivos Colombianos a través del proceso de minería de datos CRISP-DM. En este trabajo se definió un conjunto de datos con el objetivo de generar clasificadores precisos; una vez construido el conjunto de datos, fue probado mediante tres clasificadores: Maquinas de vector de regresión, Redes neuronales con propagación hacia atrás y Árboles de regresión

Using machine learning tools for protein database biocuration assistance

Biocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases becomes a central task in biological knowledge dissemination. One relevant challenge for biocurators is the unambiguous identification of biological entities. In this study, we illustrate the adequacy of machine learning methods as biocuration assistance tools using a publicly available protein database as an example. This database contains information on G Protein-Coupled Receptors (GPCRs), which are part of eukaryotic cell membranes and relevant in cell communication as well as major drug targets in pharmacology. These receptors are characterized according to subtype labels. Previous analysis of this database provided evidence that some of the receptor sequences could be affected by a case of label noise, as they appeared to be too consistently misclassified by machine learning methods. Here, we extend our analysis to recent and quite substantially modified new versions of the database and reveal their now extremely accurate labeling using several machine learning models and different transformations of the unaligned sequences. These findings support the adequacy of our proposed method to identify problematic labeling cases as a tool for database biocuration.Peer ReviewedPostprint (published version

3D Classification of Power Line Scene Using Airborne Lidar Data

Failure to adequately maintain vegetation within a power line corridor has been identified as a main cause of the August 14, 2003 electric power blackout. Such that, timely and accurate corridor mapping and monitoring are indispensible to mitigate such disaster. Moreover, airborne LiDAR (Light Detection And Ranging) has been recently introduced and widely utilized in industries and academies thanks to its potential to automate the data processing for scene analysis including power line corridor mapping. However, today’s corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for the large-scale, rapid commercial compilation of corridor maps. Additionally, in academies only few studies have developed algorithms capable of recognizing corridor objects in the power line scene, which are mostly based on 2-dimensional classification. Thus, the objective of this dissertation is to develop a 3-dimensional classification system which is able to automatically identify key objects in the power line corridor from large-scale LiDAR data. This dissertation introduces new features for power structures, especially for the electric pylon, and existing features which are derived through diverse piecewise (i.e., point, line and plane) feature extraction, and then constructs a classification model pool by building individual models according to the piecewise feature sets and diverse voltage training samples using Random Forests. Finally, this dissertation proposes a Multiple Classifier System (MCS) which provides an optimal committee of models from the model pool for classification of new incoming power line scene. The proposed MCS has been tested on a power line corridor where medium voltage transmission lines (115 kV and 230 kV) pass. The classification results based on the MCS applied by optimally selecting the pre-built classification models according to the voltage type of the test corridor demonstrate a good accuracy (89.07%) and computationally effective time cost (approximately 4 hours/km) without additional training fees

Hierarchical ensemble methods for protein function prediction

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware \u201cflat\u201d prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a \u201cconsensus\u201d ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research

Voting with Random Classifiers (VORACE)

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets