Mario principal research interests is in the development on classifiers and regressors based on Genetic Programming that can work on multi-dimensional problems with thousands of examples (see EvoDAG).
INGEOTEC research interest is text categorization seen as a supervised learning problem, that is, as a classification task. In this problem, we have developed two text modeling techniques that represent the text in a vector space model and use a Support Vector Machine as a classifier. These techniques are B4MSA which is a sentiment analysis classifier and microTC a general text classifier. In addtion this, we have been working on novel classifiers based on Genetic Programming EvoDAG.
In sentiment analysis, we have participated in a number of sentiment analysis competitions such as:
In order to facilitate and encourage the reproducibility of our research, we have decided to make the software available with an open source license. We have decided to implement our developments in Python following some continuous integration techniques (using travis-ci.org), unit testing (using nose), and coverage (using Coveralls).
Evolving Directed Acyclic Graph (EvoDAG) is a steady-state Genetic Programming system with tournament selection. The main characteristic of EvoDAG is that the genetic operation is performed at the root. EvoDAG was inspired by the geometric semantic crossover proposed by Alberto Moraglio et al. and the implementation performed by Leonardo Vanneschi et al.
EvoDAG is described in the following conference paper EvoDAG: A semantic Genetic Programming Python library Mario Graff, Eric S. Tellez, Sabino Miranda-Jiménez, Hugo Jair Escalante. 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) pp 1-6. A pre-print version can be download from here.
B4MSA is a Python Sentiment Analysis Classifier for Twitter-like short texts. It can be used to create a first approximation to a sentiment classifier on any given language. It is almost language-independent, but it can take advantage of the particularities of a language.
microTC follows a minimalistic approach to text classification.
It is designed to tackle text-classification problems in an agnostic way,
being both domain and language independent.
Currently, we only produce single-label classifiers; but support for multi-labeled problems is in the roadmap.
112 Circuito Tecnopolo Norte Col. Tecnopolo Pocitos II, C.P. 20313, Aguascalientes, Ags, México.
Tel. +52 (555) 624 28 00 Ext. 6315
email: mario.graff at infotec.mx