131 research outputs found
Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach
No full textWe propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define "Approximation Usefulness" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].References[1] Mohammed Ghaith Altarabichi, Yuantao Fan, Sepideh Pashami, Peyman Sheikholharam Mashhadi, and Sławomir Nowaczyk. 2021. Extracting invariant features for predicting state of health of batteries in hybrid energy buses. In 2021 ieee 8th international conference on data science and advanced analytics (dsaa). IEEE, 1–6.[2] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2021. Surrogate-assisted genetic algorithm for wrapper feature selection. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 776–785.[3] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2023. Fast Genetic Algorithm for feature selection—A qualitative approximation approach. Expert systems with applications 211 (2023), 118528.© 2023 Copyright held by the owner/author(s).</p
Spaces of contestation: the everyday experiences of ten African migrants in Cape Town
No full textIncludes bibliographical references.Xenophobia in South Africa is so overt that it has take a covert form. The 'xenocide' events that took place in 2008 were called xenophobic acts. It is the recurrent denialism of xenophobia on an everyday basis that this project has explored through the narrative accounts of ten African migrants in Cape Town. The lived everyday experiences of ten African migrants have brought forward the central argument of this thesis. From the data, it is evident that as a reponse to everyday pressures of prejudices and xenophobia in social and physical spaces, African migrants have developed mutable, unsettled and vagrant identities in order to cope with everyday low level violence. This argument emerged as four key stressors have been identified as the components of a more substantial explanation of xenophobia in South Africa. The four key components are: the enforcement of identity (national and group), the demarcation of spaces of belonging, the experiences of economic insecurity, and lastly a 'culture of violence' in South Africa. This thesis argues that these four stressors are the result of an on-going active process of xenophobic attitudes
Sublinear algorithms for massive data problems
No full textThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 227-244).In this thesis, we present algorithms and prove lower bounds for fundamental computational problems in the models that address massive data sets. The models include streaming algorithms, sublinear time algorithms, property testing algorithms, sublinear query time algorithms with preprocessing, or computing small summaries for large data. More precisely, we study the following problems. The (Approximate) Nearest Neighbor problem models the task of searching among a large data set of objects. Given a data set of n points in a high dimensional space, its goal is to search for the closest point in the data set to a given query point, in sublinear time, and by suitably preprocessing the data. This problem has numerous applications in image and video databases, information retrieval, clustering, and many others. In these applications, the points model the objects in a large data set, and their closeness measure similarity between the objects. However, for the purpose of many applications, the basic formulation of Nearest Neighbor as described, encounters several challenges which we address in this thesis: we show how to deal with the case where the data is corrupted or incomplete, how to handle multiple related queries, and how to handle a data set of more complex objects rather than simple points. Next, we show a general approach for solving massive data problems. We introduce the notion of Composable Coresets, defined as small summaries of multiple data sets that can be aggregated together to summarize the whole data. We show how to compute such summaries for several clustering problems, and at the same time, demonstrate that no such summaries are possible for other natural problems such as maximum coverage. Finally, we study the Set Cover problem in alternate sublinear models: streaming algorithms (where one makes a small number of passes over the data using small storage), and sublinear time algorithms (where one computes the answer without reading the whole input). We present tight approximation algorithms for the Set Cover problem in both of these models. In this thesis, we introduce theoretical problems and concepts that model computational issues arising in databases, computer vision and other areas. Most of the presented algorithms are simple and practical to implement.by Sepideh Mahabadi.Ph. D
Reliable Estimation of the Intra-Voxel Incoherent Motion Parameters of Brain Diffusion Imaging Using θ-Teaching-Learning-Based Optimization
No full textIntra-voxel incoherent motion (IVIM) imaging can characterize diffusion and perfusion of tissues. Traditionally, the least-square method has been used to determine IVIM parameters consisting of pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and the micro-vascular volume fraction (f). This paper proposes an accurate estimation method for IVIM parameters in human brain tissues using θ-teaching-learning-based-optimization (θ-TLBO). θ-TLBO as an evolutionary algorithm provides high quality solutions for parameter estimations in curve fitting problems. Evaluation of the proposed method was performed on simulated data with different levels of noise and experimental data. The estimated parameters were compared with the results of TLBO and three conventional algorithms: Segmented-Unconstrained (“SU”), Segmented-Constrained (“SC”) and “Full”. The results show that the proposed θ-TLBO has higher accuracy, precision and robustness than other methods in estimating parameters of simulated and experimental data in human brain images especially in low SNR images according to analysis of variance (ANOVA), coefficient of variation (CV), relative bias and relative root mean square errors
Does the position of shoulder immobilization after reduced anterior glenohumeral dislocation affect coaptation of a Bankart lesion? An arthrographic comparison
No full textSpectrum of Central Nervous System Anomalies Detected by Fetal Magnetic Resonance Imaging; A 2 Year Study
No full textFetal Central Nervous System Anomalies Detected by Magnetic Resonance Imaging: A Two-Year Experience
No full textFunctioning adrenocortical oncocytoma: A case report and review of literature
No full textAdrenocortical oncocytoma is very rare. Less than five functioning types of them are reported and most of the reported cases are incidentally found. We herein report a case of functioning adrenocortical oncocytoma of the left adrenal cortex in a young woman
Comparison of the Accuracy of DWI and Ultrasonography in Screening Hepatocellular Carcinoma in Patients with Chronic Liver Disease
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