7,706 research outputs found
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
Guest editorial foreword to the special issue on intelligent computation for bioinformatics
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A graph-based representation of Gene Expression profiles in DNA microarrays
This paper proposes a new and very flexible data model, called gene expression graph (GEG), for genes expression analysis and classification. Three features differentiate GEGs from other available microarray data representation structures: (i) the memory occupation of a GEG is independent of the number of samples used to built it; (ii) a GEG more clearly expresses relationships among expressed and non expressed genes in both healthy and diseased tissues experiments; (iii) GEGs allow to easily implement very efficient classifiers. The paper also presents a simple classifier for sample-based classification to show the flexibility and user-friendliness of the proposed data structur
Evolutionary NAS with Gene Expression Programming of Cellular Encoding
The renaissance of neural architecture search (NAS) has seen classical
methods such as genetic algorithms (GA) and genetic programming (GP) being
exploited for convolutional neural network (CNN) architectures. While recent
work have achieved promising performance on visual perception tasks, the direct
encoding scheme of both GA and GP has functional complexity deficiency and does
not scale well on large architectures like CNN. To address this, we present a
new generative encoding scheme --
(SLGE) -- simple, yet powerful scheme which embeds local graph transformations
in chromosomes of linear fixed-length string to develop CNN architectures of
variant shapes and sizes via evolutionary process of gene expression
programming. In experiments, the effectiveness of SLGE is shown in discovering
architectures that improve the performance of the state-of-the-art handcrafted
CNN architectures on CIFAR-10 and CIFAR-100 image classification tasks; and
achieves a competitive classification error rate with the existing NAS methods
using less GPU resources.Comment: Accepted at IEEE SSCI 2020 (7 pages, 3 figures
Interpretable Categorization of Heterogeneous Time Series Data
Understanding heterogeneous multivariate time series data is important in
many applications ranging from smart homes to aviation. Learning models of
heterogeneous multivariate time series that are also human-interpretable is
challenging and not adequately addressed by the existing literature. We propose
grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs
extend decision trees with a grammar framework. Logical expressions derived
from a context-free grammar are used for branching in place of simple
thresholds on attributes. The added expressivity enables support for a wide
range of data types while retaining the interpretability of decision trees. In
particular, when a grammar based on temporal logic is used, we show that GBDTs
can be used for the interpretable classi cation of high-dimensional and
heterogeneous time series data. Furthermore, we show how GBDTs can also be used
for categorization, which is a combination of clustering and generating
interpretable explanations for each cluster. We apply GBDTs to analyze the
classic Australian Sign Language dataset as well as data on near mid-air
collisions (NMACs). The NMAC data comes from aircraft simulations used in the
development of the next-generation Airborne Collision Avoidance System (ACAS
X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data
Mining (SDM) 201
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