2,455 research outputs found
Ensemble Learning for Free with Evolutionary Algorithms ?
Evolutionary Learning proceeds by evolving a population of classifiers, from
which it generally returns (with some notable exceptions) the single
best-of-run classifier as final result. In the meanwhile, Ensemble Learning,
one of the most efficient approaches in supervised Machine Learning for the
last decade, proceeds by building a population of diverse classifiers. Ensemble
Learning with Evolutionary Computation thus receives increasing attention. The
Evolutionary Ensemble Learning (EEL) approach presented in this paper features
two contributions. First, a new fitness function, inspired by co-evolution and
enforcing the classifier diversity, is presented. Further, a new selection
criterion based on the classification margin is proposed. This criterion is
used to extract the classifier ensemble from the final population only
(Off-line) or incrementally along evolution (On-line). Experiments on a set of
benchmark problems show that Off-line outperforms single-hypothesis
evolutionary learning and state-of-art Boosting and generates smaller
classifier ensembles
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
Tiny Classifier Circuits: Evolving Accelerators for Tabular Data
A typical machine learning (ML) development cycle for edge computing is to
maximise the performance during model training and then minimise the
memory/area footprint of the trained model for deployment on edge devices
targeting CPUs, GPUs, microcontrollers, or custom hardware accelerators. This
paper proposes a methodology for automatically generating predictor circuits
for classification of tabular data with comparable prediction performance to
conventional ML techniques while using substantially fewer hardware resources
and power. The proposed methodology uses an evolutionary algorithm to search
over the space of logic gates and automatically generates a classifier circuit
with maximised training prediction accuracy. Classifier circuits are so tiny
(i.e., consisting of no more than 300 logic gates) that they are called "Tiny
Classifier" circuits, and can efficiently be implemented in ASIC or on an FPGA.
We empirically evaluate the automatic Tiny Classifier circuit generation
methodology or "Auto Tiny Classifiers" on a wide range of tabular datasets, and
compare it against conventional ML techniques such as Amazon's AutoGluon,
Google's TabNet and a neural search over Multi-Layer Perceptrons. Despite Tiny
Classifiers being constrained to a few hundred logic gates, we observe no
statistically significant difference in prediction performance in comparison to
the best-performing ML baseline. When synthesised as a Silicon chip, Tiny
Classifiers use 8-18x less area and 4-8x less power. When implemented as an
ultra-low cost chip on a flexible substrate (i.e., FlexIC), they occupy 10-75x
less area and consume 13-75x less power compared to the most hardware-efficient
ML baseline. On an FPGA, Tiny Classifiers consume 3-11x fewer resources.Comment: 14 pages, 16 figure
A tandem evolutionary algorithm for identifying causal rules from complex data
We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease
Evolving artificial cell signaling networks using molecular classifier systems
Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs
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