Papers by Jose Francisco Martinez Trinidad
IEEE Access
Nowadays, the international scientific community of machine learning has an enormous campaign in ... more Nowadays, the international scientific community of machine learning has an enormous campaign in favor of creating understandable models instead of black-box models. The main reason is that experts in the application area are showing reluctance due to black-box models cannot be understood by them, and consequently, their results are difficult to be explained. In unsupervised problems, where experts have not labeled objects, obtaining an explanation of the results is necessary because specialists in the application area need to understand both the applied model as well as the obtained results for finding the rationale behind each obtained clustering from a practical point of view. Hence, in this paper, we introduce a clustering based on decision trees (eUD3.5), which builds several decision trees from numerical databases. Unlike previous solutions, our proposal takes into account both separation and compactness for evaluating a feature split without decreasing time efficiency and with no empirical parameter to control the depth of the trees. We tested eUD3.5 on 40 numerical databases of UCI Machine Learning Repository, showing that our proposal builds a set of high-quality unsupervised decision trees for clustering, allowing us to obtain the best average ranking compared with other popular state-of-the-art clustering solutions. Also, from the collection of unsupervised decision trees induced by our proposal, a set of high-quality patterns are extracted for showing the main feature-value pairs describing each cluster. INDEX TERMS Explainable model, clustering, unsupervised decision trees, numerical databases.
Gate Detection for Micro Aerial Vehicles using a Single Shot Detector
IEEE Latin America Transactions
Algorithms for mining frequent itemsets in static and dynamic datasets
Intelligent Data Analysis
ABSTRACT
CAR-NF: A classifier based on specific rules with high netconf
Intelligent Data Analysis
ABSTRACT
Building fast decision trees from large training sets
Intelligent Data Analysis
ABSTRACT Decision trees are commonly used in supervised classification. Currently, supervised cla... more ABSTRACT Decision trees are commonly used in supervised classification. Currently, supervised classification problems with large training sets are very common, however many supervised classifiers cannot handle this amount of data. There are some decision tree induction algorithms that are capable to process large training sets, however almost all of them have memory restrictions because they need to keep in main memory the whole training set, or a big amount of it. Moreover, algorithms that do not have memory restrictions have to choose a subset of the training set, needing extra time for this selection; or they require to specify the values for some parameters that could be very difficult to determine by the user. In this paper, we present a new fast heuristic for building decision trees from large training sets, which overcomes some of the restrictions of the state of the art algorithms, using all the instances of the training set without storing all of them in main memory. Experimental results show that our algorithm is faster than the most recent algorithms for building decision trees from large training sets.
Linear model optimizer vs Neural Networks: A comparison for improving the quality and saving of LED-Lighting control systems
2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Compresión De Imágenes Sin Pérdida Usando Clasificador 1-NN Para Adaptar Los Coeficientes De Filtros Lifting
DYNA INGENIERIA E INDUSTRIA, 2017
Ajuste De Filtros Wavelet Utilizando K-NN Para Compresión De Imágenes Sin Perdida
DYNA NEW TECHNOLOGIES, 2016
Métodos para la selección de prototipos
Computacion Y Sistemas, Jun 1, 2010
Ciarp, 2004
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Computacion Y Sistemas, Dec 1, 2011
Frequent connected subgraph (FCS) mining is an interesting problem with wide applications in real... more Frequent connected subgraph (FCS) mining is an interesting problem with wide applications in real life. Most of the FCS mining algorithms have been focused on detecting duplicate candidates using canonical form tests. Canonical form tests have high computational complexity, and therefore, they affect the efficiency of graph miners. In this paper, we introduce novel properties to reduce the number of canonical form tests in FCS mining. Based on these properties, a new algorithm for FCS mining called gRed is presented. The experimentation on real world datasets shows the impact of the proposed properties on the efficiency of gRed reducing the number of canonical form tests regarding gSpan. Besides, the performance of our algorithm is compared against gSpan and other state-of-the-art algorithms.
Pattern Recognition - 4th Mexican Conference, MCPR 2012, Huatulco, Mexico, June 27-30, 2012. Proceedings
ABSTRACT For the preceding conference see Zbl 1216.68034.
C-Means Algorithm whit Similarity Functions
Computacion Y Sistemas, May 10, 2009
Computación y Sistemas Voi. 5 No. 4 pp. 241 - 246 2002, СЮ - IPN. ISSN /405-5546 Impreso en Méxic... more Computación y Sistemas Voi. 5 No. 4 pp. 241 - 246 2002, СЮ - IPN. ISSN /405-5546 Impreso en México C-Means Algorithm with Similarity Functions Algoritmo C-means con Funciones de Similaridad José Francisco Martínez Trinidad', Javier Raymundo García Serrano2 and ...
Una Metodología Unificada para la Evaluación de Algoritmos de Clasificación tanto Supervisados como No-Supervisados
Computacion Y Sistemas, Jun 1, 2006
... Transword Research Networks, USA, 2002, 133-176. 3. J. Ruiz Shulcloper, Eduardo A. Cabrera, M... more ... Transword Research Networks, USA, 2002, 133-176. 3. J. Ruiz Shulcloper, Eduardo A. Cabrera, Manuel Lazo Cortés Introducción al Reconocimiento de Patrones (Enfoque Lógico-Combinatorio) CINVESTAV-IPN Serie Verde No. 51, México, 1995. ...
Editing and Training for ALVOT, an Evolutionary Approach
Ideal, 2003
Bases Conceptuales para una Teoría de Objetos Simbólicos
Computacion Y Sistemas, May 10, 2009
Clasificadores basados en arboles de decisión para grandes conjuntos de datos
LC: A Conceptual Clustering Algorithm
Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, 2001
Page 1. P. Perner (Ed.): MLDM 2001, LNAI 2123, pp. 117127, 2001. © Springer-Verlag Berlin Heidel... more Page 1. P. Perner (Ed.): MLDM 2001, LNAI 2123, pp. 117127, 2001. © Springer-Verlag Berlin Heidelberg 2001 LC: A Conceptual Clustering Algorithm José Fco. Martínez-Trinidad1 and Guillermo Sánchez-Díaz2 1Centro de ...
Algoritmos Conceptuales Restringidos basados en Semillas
Computacion Y Sistemas, Dec 1, 2007
Editorial Special Issue MCPR 2014: Advances in pattern recognition methodologies and applications
Neurocomputing, 2015
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Papers by Jose Francisco Martinez Trinidad