Mill.jl and JsonGrinder.jl: automated differentiable feature extraction
for learning from raw JSON data
- URL: http://arxiv.org/abs/2105.09107v1
- Date: Wed, 19 May 2021 13:02:10 GMT
- Title: Mill.jl and JsonGrinder.jl: automated differentiable feature extraction
for learning from raw JSON data
- Authors: Simon Mandlik, Matej Racinsky, Viliam Lisy, Tomas Pevny
- Abstract summary: Learning from raw data input is one of the key components of successful applications of machine learning methods.
Learning from raw data input is one of the key components of successful applications of machine learning methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Learning from raw data input, thus limiting the need for manual feature
engineering, is one of the key components of many successful applications of
machine learning methods. While machine learning problems are often formulated
on data that naturally translate into a vector representation suitable for
classifiers, there are data sources, for example in cybersecurity, that are
naturally represented in diverse files with a unifying hierarchical structure,
such as XML, JSON, and Protocol Buffers. Converting this data to vector
(tensor) representation is generally done by manual feature engineering, which
is laborious, lossy, and prone to human bias about the importance of particular
features.
Mill and JsonGrinder is a tandem of libraries, which fully automates the
conversion. Starting with an arbitrary set of JSON samples, they create a
differentiable machine learning model capable of infer from further JSON
samples in their raw form.
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