J. Arturo Olvera-López
National Institute of Astrophysics, Optics and Electronics
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Publication
Featured researches published by J. Arturo Olvera-López.
Artificial Intelligence Review | 2010
J. Arturo Olvera-López; J. Ariel Carrasco-Ochoa; J. Francisco Martínez-Trinidad; Josef Kittler
In supervised learning, a training set providing previously known information is used to classify new instances. Commonly, several instances are stored in the training set but some of them are not useful for classifying therefore it is possible to get acceptable classification rates ignoring non useful cases; this process is known as instance selection. Through instance selection the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers. This work is focused on presenting a survey of the main instance selection methods reported in the literature.
Pattern Analysis and Applications | 2010
J. Arturo Olvera-López; J. Ariel Carrasco-Ochoa; J. Francisco Martínez-Trinidad
In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.
iberoamerican congress on pattern recognition | 2008
J. Arturo Olvera-López; J. Ariel Carrasco-Ochoa; J. Fco. Martínez-Trinidad
In Pattern recognition, the supervised classifiers use a training set Tfor classifying new prototypes. In practice, not all information in Tis useful for classification therefore it is necessary to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time in the training and/or classification stages could be reduced. Several prototype selection methods have been proposed following the Nearest Neighbor (NN) rule; in this work, we propose a new prototype selection method based on the prototype relevance and border prototypes, which is faster (over large datasets) than the other tested prototype selection methods. We report experimental results showing the effectiveness of our method and compare accuracy and runtimes against other prototype selection methods.
Pattern Recognition | 2013
Pablo Hernandez-Leal; J. Ariel Carrasco-Ochoa; J. Fco. Martínez-Trinidad; J. Arturo Olvera-López
Instance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per class using Borders (instances near to instances belonging to different classes), using this ranking we propose an instance selection algorithm (IRB). We evaluated the proposed algorithm using k-NN with small and large datasets, comparing it against state of the art instance selection algorithms. In our experiments, for large datasets IRB has the best compromise between time and accuracy. We also tested our algorithm using SVM, LWLR and C4.5 classifiers, in all cases the selection computed by our algorithm obtained the best accuracies in average.
computer recognition systems | 2007
J. Arturo Olvera-López; J. Ariel Carrasco-Ochoa; J. Francisco Martínez-Trinidad
Object selection is an important task for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new method based on clustering which tries to find border objects that contribute with useful information allowing to the classifier discriminating between classes. An experimental comparison of our method, the CLU method based on clustering, and the DROP methods, is presented.
machine learning and data mining in pattern recognition | 2007
J. Arturo Olvera-López; J. Francisco Martínez-Trinidad; J. Ariel Carrasco-Ochoa
The object selection is an important task for instance-based classifiers since through this process the size of a training set could be reduced and then the runtimes in both classification and training steps would be reduced. Several methods for object selection have been proposed but some methods discard relevant objects for the classification step. In this paper, we propose an object selection method which is based on the idea of sequential floating search. This method reconsiders the inclusion of relevant objects previously discarded. Some experimental results obtained by our method are shown and compared against some other object selection methods.
iberoamerican congress on pattern recognition | 2007
J. Arturo Olvera-López; J. Francisco Martínez-Trinidad; J. Ariel Carrasco-Ochoa
In supervised classification, the object selection or instance selection is an important task, mainly for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers.
iberoamerican congress on pattern recognition | 2005
J. Arturo Olvera-López; J. Fco. Martínez-Trinidad; J. Ariel Carrasco-Ochoa
Edition is an important and useful task in supervised classification specifically for instance-based classifiers because edition discards from the training set those useless or harmful objects for the classification accuracy and it helps to reduce the size of the original training sample and to increase both the classification speed and accuracy. In this paper, we propose two edition schemes that combine edition methods and sequential search for instance selection. In addition, we present an empirical comparison between these schemes and some other edition methods.
mexican conference on pattern recognition | 2017
Jesús García-Ramírez; J. Arturo Olvera-López; Ivan Olmos-Pineda; Manuel Martín-Ortíz
In computer vision, facial images have several applications such as Facial Expression Recognition and Face Recognition. The segmentation of Regions Of Interest (ROIs) in face images are relevant, because those provide information about facial expressions. In this paper a method to segment mouth and eyebrows in face images based on edge detection and pixel density is proposed. According to the experimental results, our approach extracts the ROIs in face images taken from different public datasets.
international conference on human computer interaction | 2017
J. Emmanuel Vázquez-Valencia; Manuel Martín-Ortíz; Ivan Olmos-Pineda; J. Arturo Olvera-López; David E. Pinto-Avendaño
In this work we propose an approach for controlling a wheel chair using Motion Capture, particularly gestures from the face are considered as commands for the basic control operations required for driving a wheelchair. Gesture recognition is carried out training an Artificial Neural Network, which is one of the most successful classifier in Pattern Recognition. Based on our experimentation, the proposed approach was able to detect gestures related to order commands which is useful for controlling a wheelchair by users with restricted mobility or disability in legs, arms or hands.