36 research outputs found
Feature Engineering for Predictive Modeling using Reinforcement Learning
Feature engineering is a crucial step in the process of predictive modeling.
It involves the transformation of given feature space, typically using
mathematical functions, with the objective of reducing the modeling error for a
given target. However, there is no well-defined basis for performing effective
feature engineering. It involves domain knowledge, intuition, and most of all,
a lengthy process of trial and error. The human attention involved in
overseeing this process significantly influences the cost of model generation.
We present a new framework to automate feature engineering. It is based on
performance driven exploration of a transformation graph, which systematically
and compactly enumerates the space of given options. A highly efficient
exploration strategy is derived through reinforcement learning on past
examples
Bioinformatics and Medicine in the Era of Deep Learning
Many of the current scientific advances in the life sciences have their
origin in the intensive use of data for knowledge discovery. In no area this is
so clear as in bioinformatics, led by technological breakthroughs in data
acquisition technologies. It has been argued that bioinformatics could quickly
become the field of research generating the largest data repositories, beating
other data-intensive areas such as high-energy physics or astroinformatics.
Over the last decade, deep learning has become a disruptive advance in machine
learning, giving new live to the long-standing connectionist paradigm in
artificial intelligence. Deep learning methods are ideally suited to
large-scale data and, therefore, they should be ideally suited to knowledge
discovery in bioinformatics and biomedicine at large. In this brief paper, we
review key aspects of the application of deep learning in bioinformatics and
medicine, drawing from the themes covered by the contributions to an ESANN 2018
special session devoted to this topic