Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming

Rodriguez-Coayahuitl, Lino; Morales-Reyes, Alicia

doi:10.1007/978-3-319-77553-1_17

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…Evolutionary Computation (EC) has seen very recent use in evolving autoencoders for image classification tasks using Genetic Algorithms [19], GP [16], and Particle Swarm Optimisation [18]. Historically, auto-encoders have had to be manually designed or require significant domain knowledge to get good results, and so automatic evolution of auto-encoder structure is a clear improvement.…”

Section: Related Workmentioning

confidence: 99%

Can Genetic Programming Do Manifold Learning Too?

Lensen

Xue

Zhang

2019

Lecture Notes in Computer Science

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Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.

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Section: Related Workmentioning

confidence: 99%

Can Genetic Programming Do Manifold Learning Too?

Lensen

Xue

Zhang

2019

Lecture Notes in Computer Science

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“…Rodriguez-Coayahuitl [30] defined GP for representation learning and proposed GP autoencoder for unsupervised representation learning for image classification. Similar to autoencoder, a GP encoding forest was used for encoding and a GP decoding forest was used for decoding, which means each GP individual contains two forests with a number of trees.…”

Section: B Gp-based Edl Methodsmentioning

confidence: 99%

An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

Xue

Zhang

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.

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“…The use of Evolutionary Computing (EC) for machine learning is presented from various perspectives such as feature selection and classification, regression and deep learning [79]. In deep learning the EC provides an optimal solution to machine learning for reducing the cost such as a significant amount of time and domain expertise [80]- [82]. Bui et al [83] proposed a flash prediction model using Particle Swarm Optimization (PSO) in an extreme learning machine that delivers the weights efficiently.…”

Section: Related Workmentioning

confidence: 99%

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

2020

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Safe Routes to School is very important for students to have good physical and psychologically healthy in school life. For providing safe routes based on risk analysis, finding out dangerous points and areas can be a target to avoid dangerous locations by pedestrians and drivers. However, analyzing the risk assessment to derive the safe routes requires a large amount of data with a certain time of observation by experts. Deep learning is a solution to provide information regarding safe routes based on expert knowledge. In this paper, we propose a risk assessment inference approach using a Recurrent Neural Network (RNN) model with Long-Short Term Memory (LSTM) cells based on geographical information for safe routes to school. However, geographical information including coordinates is difficult used in learning-based inference models because of the series of float values. For training the RNN model with the geographical data, coordinates of routes and danger points are translated to be geohash through the geohash converter. The geohash data with other data of features are fused and inputted to the one-hot encoder. The one-hot encoded data is used in the inputs of the RNN model to train the LSTMs. The input data of the training model is derived by the risk index model that is proposed to calculate the risk index based on distances of route coordinates and danger points. Therefore, the risk index is correlated with the training dataset. Through the proposed inference approach, the geographical information including multiple coordinates is enabled to be trained by RNN as a geohash-based input string. Moreover, the input string with other features is fused to support the one-hot encoding to get a better result in RNN models.

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Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming

Cited by 18 publications

References 19 publications

Can Genetic Programming Do Manifold Learning Too?

Can Genetic Programming Do Manifold Learning Too?

An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

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