Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection

Resumen

With the increase of computational power and memory capacity, it is possible to record and analyse lots of features of different nature in real time or in a data stream manner. Nonetheless, in many applications, not all variables may be relevant to explain the nature of the data. Feature subset selection strategies are therefore required to extract the relevant features to understand the data generating process. Much work has been performed in the area of supervised classification problems regarding feature selection, whereas for unsupervised environments, scarce studies have been conducted. In current feature subset selection methodologies for unsupervised data streams, the set of relevant variables are reevaluated or forgotten whenever a new instance or chunk of data arrives: this approach can be computationally expensive or unnecessary. Therefore, in this article, we provide a method to perform feature subset selection in unsupervised data streams. An embedded feature subset selection methodology based on asymmetric hidden Markov models and novel concept discovery strategies is used. This methodology can be used to detect novel concepts in data, and update the set of relevant features when a drift in the data stream is detected. Thus, the relevant variables are updated only when needed. To make this possible, we provide a model for batch and online problems, that is capable of dynamically determining the relevant variables to address feature selection in data streams. Additionally, the model provides domain insights using context-specific Bayesian networks which can be helpful to understand the dynamic process. To validate the proposed methodology, synthetic and real data from ball-bearings are used. Additionally, the proposed methodology is compared with other state of the art methodologies. The proposed method can be used to update the relevant features when needed and is more stable than its competitors.