Genetic folding for solving multiclass SVM problems

Mezher, Mohammad A.; Abbod, Maysam F.

doi:10.1007/s10489-014-0533-1

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Alternatively, hyperparameters can be also learned apart from the structure in a secondary optimization procedure. The most common hyperparameter optimization method is grid search [22,12,28,34,11], although more complex methods have been also tried, such as particle swarm optimization [44]. As can be seen, choosing the right hyperparameters for the kernel remains an open question.…”

Section: Hyperparameter Settingmentioning

confidence: 99%

“…Furthermore, it has a direct influence on the search method, as the roughness of the search landscape heavily depends on this choice. In the SVM kernel search literature, almost every proposal uses accuracy [19,24,47,34,2,48,11,44,12,49,22] or classification error related metrics [28,25,21] to measure the goodness of the kernel. As these metrics are discrete, some of the approaches include tiebreakers to deal with the same results when comparing similar kernels [25,49].…”

Section: Metricsmentioning

confidence: 99%

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In-depth analysis of SVM kernel learning and its components

Roman

Santana

Mendiburu

et al. 2020

Neural Comput & Applic

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The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learning of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an intricate way and it is di cult to provide a comprehensible analysis of their joint e↵ect. In this paper we try to fill this gap introducing the necessary steps in order to understand these interactions and provide clues for the research community to know where to place the emphasis. First of all, we identify all the factors that a↵ect the final performance of support vector machines in relation to the elicitation of kernels. Next, we analyze the factors independently or in pairs and study the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support vector machines.

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Section: Hyperparameter Settingmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

In-depth analysis of SVM kernel learning and its components

Roman

Santana

Mendiburu

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Although computer programs may be complex tree structures GF could represent the program by learning and adapting their sizes. In [4][5][6][7][8], all papers addressed the GF chromosome, as it is the standard GF chromosome genotype format. However, in this paper we introduced GF algorithm for a new sort of genotype which being used for three functions and three terminals including three string styles.…”

Section: Genetic Folding Chromosomementioning

confidence: 99%

“…GF algorithm shown an effective strategy in various types of computer problems such as binary and multi-classification and regression datasets. For example, GF for binary classification [7], multi-classification [5] and regression [6] have demonstrated how GF used to derive superior results in comparing to other members of the evolutionary algorithm's family. Fig.…”

Section: Introductionmentioning

confidence: 99%

A Novel Genetic Operator for Genetic Folding Algorithm: A Refolding Operator and a New Genotype

Mezher¹,

Abbod²

2017

IJEM

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Genetic Folding algorithm uses linear chromosomes composed of organized genes in floating-numbers manner, in which each genes chain fold back on themselves to form the final GF chromosome. In this paper, a novel genotype representation and a novel genetic operator were proposed. The paper was applied using MATLAB code to illustrate the beneficiary, flexibility and powerful of the Genetic Folding algorithm solving Santa Fe Trail problem. The problem of programming an artificial ant to follow the Santa Fe Trail is used as an example of program search space.To evaluate the efficiency and feasibility of the proposed methods, a comparison was held between the various types and sizes through the Santa Fe Trail problem. Several test functions along with various levels of difficulty were also conducted. Results of this proposal clearly show significant results of the proposed genotype and the genetic operator also.

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“…The GF approach has shown to be effective in various computer problems, including binary, multi-classification, and regression datasets. For example, GF has been proven to outperform other members of the evolutionary algorithm family in binary classification, multi-classification ( Mezher & Abbod, 2014 ), and regression ( Mezher & Abbod, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

An enhanced Genetic Folding algorithm for prostate and breast cancer detection

Mezher

Altamimi

2022

PeerJ Computer Science

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Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model’s area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.

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Genetic folding for solving multiclass SVM problems

Cited by 5 publications

References 6 publications

In-depth analysis of SVM kernel learning and its components

In-depth analysis of SVM kernel learning and its components

A Novel Genetic Operator for Genetic Folding Algorithm: A Refolding Operator and a New Genotype

An enhanced Genetic Folding algorithm for prostate and breast cancer detection

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