Abstract-A filter method of feature selection based on mutual information, called normalized mutual information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti's MIFS, MIFS-U, and mRMR methods. The average normalized mutual information is proposed as a measure of redundancy among features. NMIFS outperformed MIFS, MIFS-U, and mRMR on several artificial and benchmark data sets without requiring a user-defined parameter. In addition, NMIFS is combined with a genetic algorithm to form a hybrid filter/wrapper method called GAMIFS. This includes an initialization procedure and a mutation operator based on NMIFS to speed up the convergence of the genetic algorithm. GAMIFS overcomes the limitations of incremental search algorithms that are unable to find dependencies between groups of features.Index Terms-Feature selection, genetic algorithms, multilayer perceptron (MLP) neural networks, normalized mutual information (MI).

Artículo de publicación ISIIn this work, we present a review of the state of the art of information-theoretic feature selection methods. The concepts of feature relevance, redundance, and complementarity (synergy) are clearly defined, as well as Markov blanket. The problem of optimal feature selection is defined. A unifying theoretical framework is described, which can retrofit successful heuristic criteria, indicating the approximations made by each method. A number of open problems in the field are presented.CONICYT-CHILE under grant FONDECYT 1110701

In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.

Type II supernovae (SNe) originate from the explosion of hydrogen-rich supergiant massive stars. Their first electromagnetic signature is the shock breakout, a short-lived phenomenon which can last from hours to days depending on the density at shock emergence. We present 26 rising optical light curves of SN II candidates discovered shortly after explosion by the High cadence Transient Survey (HiTS) and derive physical parameters based on hydrodynamical models using a Bayesian approach. We observe a steep rise of a few days in 24 out of 26 SN II candidates, indicating the systematic detection of shock breakouts in a dense circumstellar matter consistent with a mass loss rateṀ > 10 −4 M yr −1 or a dense atmosphere. This implies that the characteristic hour timescale signature of stellar envelope SBOs may be rare in nature and could be delayed into longer-lived circumstellar material shock breakouts in most Type II SNe.With a new generation of large etendue facilities such as iPTF 1 , SkyMapper 2 , Pan-STARRS 3 , KMTNET 4 , ATLAS 5 , DECam 6 , Hyper Suprime-Cam 7 , ZTF 8 or LSST 9 the study of rare and shortlived phenomena in large volumes of the Universe is becoming possible. This allows not only finding new classes of events, but also systematically studying short-lived phases of evolution in known astrophysical phenomena, such as SN explosions. In this work we present 26 rising optical light curves from Type IIP/L 140003.