Feature construction as a bi-level optimization problem

Hammami, Marwa; Bechikh, Slim; Louati, Ali; Makhlouf, Mohamed; Saïd, Lamjed Ben

doi:10.1007/s00521-020-04784-z

Cited by 15 publications

(6 citation statements)

References 31 publications

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“…The three attribute selected methods used are recursive feature elimination (RFE), Lasso and Ridge, and RF selector, which also calculate the average of every algorithm. The user selects fifteen of the twenty attributes using feature selection 21 . The PCA is the attribute extraction method used to minimize the variables from 20 to 16.…”

Section: Related Workmentioning

confidence: 99%

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Price forecasting for real estate using machine learning: A case study on Riyadh city

Louati

Lahyani

Aldaej

et al. 2021

Concurrency and Computation

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Real estate is potentially contributing to the economic growth. It has a strong correlation between property owners and beneficiaries. The accurate forecast of future property prices is particularly important. Therefore, real estate prices are reflecting the economic level of counties, and their price ranges are of great interest to both buyers and sellers. Developing a land-price forecast model could significantly assist in predicting future land prices and setting real estate regulations. In contrast, machine learning (ML) algorithms have demonstrated a great potential to perform predictions.Motivated by these assumptions, we develop in this article a set of ML algorithms to build models capable to increase the effectiveness of land price estimation. The ML algorithms adopted in this work include the decision tree, random forest (RF), and linear regression. We collected data from 5946 lands localized in the northern area of Riyadh, KSA. This data has been collected using GeoTech's DAAL website. The performance of the developed models has been assessed based on state-of-art performance metrics including mean absolute error, mean squared error, and median squared error.The experiments show that the RF based model outperforms the remaining models.

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Section: Related Workmentioning

confidence: 99%

“…The user selects fifteen of the twenty attributes using feature selection. 21 The PCA is the attribute extraction method used to minimize the variables from 20 to 16. The researcher found, both the methods function, as well as the R, squared value of 0.86.…”

Section: Related Workmentioning

confidence: 99%

Price forecasting for real estate using machine learning: A case study on Riyadh city

Louati

Lahyani

Aldaej

et al. 2021

Concurrency and Computation

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“…The case study discussed here represents the multi-objective bi-level feature construction problem (for the binary classification case). In fact, the bi-level feature construction problem has been tackled only in a single-objective way [58]. Inspired by works from the field of evolutionary feature selection and construction [59] [60], we developed, in this paper, the multi-objective version of the bi-level feature construction problem.…”

Section: Case Study: Multi-objective Bi-level Feature Constructionmentioning

confidence: 99%

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

et al. 2020

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Many decision making situations are characterized by a hierarchical structure where a lower-level (follower) optimization problem appears as a constraint of the upper-level (leader) one. Such kind of situations is usually modeled as a BLOP (Bi-Level Optimization Problem). The resolution of the latter usually has a heavy computational cost because the evaluation of a single upper-level solution requires finding its corresponding (near) optimal lower-level one. When several objectives are optimized in each level, the BLOP becomes a multi-objective task and more computationally costly as the optimum corresponds to a whole non-dominated solution set, called the PF (Pareto Front). Despite the considerable number of recent works in multi-objective evolutionary bi-level optimization, the number of methods that could be applied to the combinatorial (discrete) case is much reduced. Motivated by this observation, we propose in this paper an Indicator-Based version of our recently proposed Co-Evolutionary Migration-Based Algorithm (CEMBA), that we name IB-CEMBA, to solve combinatorial multi-objective BLOPs. The indicator-based search choice is justified by two arguments. On the one hand, it allows selecting the solution having the maximal marginal contribution in terms of the performance indicator from the lowerlevel PF. On the other hand, it encourages both convergence and diversity at the upper-level. The comparative experimental study reveals the outperformance of IB-CEMBA on a multi-objective bi-level productiondistribution problem. From the effectiveness viewpoint, the upper-level hyper-volume values and inverted generational distance ones vary in the intervals [0.8500, 0.9710] and [0.0072, 0.2420], respectively. From the efficiency viewpoint, IB-CEMBA has a good reduction rate of the Number of Function Evaluations (NFEs), lying in the interval [30.13%, 54.09%]. To further show the versatility of our algorithm, we have developed a case study in machine learning, and more specifically we have addressed the bi-level multiobjective feature construction problem.

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“…ResNet [ 4 ], AlexNet [ 4 ] and VGGNet [ 5 ] are a few examples. Due to the fact that these structures were created manually, researchers in the fields of optimization [ 45 , 48 ] and machine learning [ 47 ] hypothesized that improved architectures could be discovered using automated methods. In fact, back propagation learning has often been shown to be inefficient in multi-layered networks due to the method being trapped in local minima by gradient descent.…”

Section: Introductionmentioning

confidence: 99%

Topology optimization search of deep convolution neural networks for CT and X-ray image classification

Louati

Bechikh

et al. 2022

BMC Med Imaging

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Covid-19 is a disease that can lead to pneumonia, respiratory syndrome, septic shock, multiple organ failure, and death. This pandemic is viewed as a critical component of the fight against an enormous threat to the human population. Deep convolutional neural networks have recently proved their ability to perform well in classification and dimension reduction tasks. Selecting hyper-parameters is critical for these networks. This is because the search space expands exponentially in size as the number of layers increases. All existing approaches utilize a pre-trained or designed architecture as an input. None of them takes design and pruning into account throughout the process. In fact, there exists a convolutional topology for any architecture, and each block of a CNN corresponds to an optimization problem with a large search space. However, there are no guidelines for designing a specific architecture for a specific purpose; thus, such design is highly subjective and heavily reliant on data scientists’ knowledge and expertise. Motivated by this observation, we propose a topology optimization method for designing a convolutional neural network capable of classifying radiography images and detecting probable chest anomalies and infections, including COVID-19. Our method has been validated in a number of comparative studies against relevant state-of-the-art architectures.

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Feature construction as a bi-level optimization problem

Cited by 15 publications

References 31 publications

Price forecasting for real estate using machine learning: A case study on Riyadh city

Price forecasting for real estate using machine learning: A case study on Riyadh city

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

Topology optimization search of deep convolution neural networks for CT and X-ray image classification

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