10,328 research outputs found
On explaining machine learning models by evolving crucial and compact features
Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
I argue that data becomes temporarily interesting by itself to some
self-improving, but computationally limited, subjective observer once he learns
to predict or compress the data in a better way, thus making it subjectively
simpler and more beautiful. Curiosity is the desire to create or discover more
non-random, non-arbitrary, regular data that is novel and surprising not in the
traditional sense of Boltzmann and Shannon but in the sense that it allows for
compression progress because its regularity was not yet known. This drive
maximizes interestingness, the first derivative of subjective beauty or
compressibility, that is, the steepness of the learning curve. It motivates
exploring infants, pure mathematicians, composers, artists, dancers, comedians,
yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007
joint invited lectur
Evolutionary approaches to explainable machine learning
Machine learning models are increasingly being used in critical sectors, but
their black-box nature has raised concerns about accountability and trust. The
field of explainable artificial intelligence (XAI) or explainable machine
learning (XML) has emerged in response to the need for human understanding of
these models. Evolutionary computing, as a family of powerful optimization and
learning tools, has significant potential to contribute to XAI/XML. In this
chapter, we provide a brief introduction to XAI/XML and review various
techniques in current use for explaining machine learning models. We then focus
on how evolutionary computing can be used in XAI/XML, and review some
approaches which incorporate EC techniques. We also discuss some open
challenges in XAI/XML and opportunities for future research in this field using
EC. Our aim is to demonstrate that evolutionary computing is well-suited for
addressing current problems in explainability, and to encourage further
exploration of these methods to contribute to the development of more
transparent, trustworthy and accountable machine learning models
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
Deep convolutional neural networks have proven their effectiveness, and have
been acknowledged as the most dominant method for image classification.
However, a severe drawback of deep convolutional neural networks is poor
explainability. Unfortunately, in many real-world applications, users need to
understand the rationale behind the predictions of deep convolutional neural
networks when determining whether they should trust the predictions or not. To
resolve this issue, a novel genetic algorithm-based method is proposed for the
first time to automatically evolve local explanations that can assist users to
assess the rationality of the predictions. Furthermore, the proposed method is
model-agnostic, i.e., it can be utilised to explain any deep convolutional
neural network models. In the experiments, ResNet is used as an example model
to be explained, and the ImageNet dataset is selected as the benchmark dataset.
DenseNet and MobileNet are further explained to demonstrate the model-agnostic
characteristic of the proposed method. The evolved local explanations on four
images, randomly selected from ImageNet, are presented, which show that the
evolved local explanations are straightforward to be recognised by humans.
Moreover, the evolved explanations can explain the predictions of deep
convolutional neural networks on all four images very well by successfully
capturing meaningful interpretable features of the sample images. Further
analysis based on the 30 runs of the experiments exhibits that the evolved
local explanations can also improve the probabilities/confidences of the deep
convolutional neural network models in making the predictions. The proposed
method can obtain local explanations within one minute, which is more than ten
times faster than LIME (the state-of-the-art method)
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