Diagnosis of breast cancer with Stacked autoencoder and Subspace kNN

Adem, Kemal

doi:10.1016/j.physa.2020.124591

Cited by 34 publications

(10 citation statements)

References 20 publications

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“…In this work, the SFSA technique is applied to select a subset of features. SFSA is based on the specific development marvel of a random fractal and basically uses two processes afterward for population initialization: (1) diffusion and (2) update to enhance the searching [24]. In the arithmetical modeling of the SFSA, the finest solution is only preferred from the diffusion method to generate novel arrangements, while overlooking discrete arrangements.…”

Section: Feature Selection Using Sfsa Techniquementioning

confidence: 99%

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

et al. 2022

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As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

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Section: Feature Selection Using Sfsa Techniquementioning

confidence: 99%

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

et al. 2022

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“…Adem [16] used stacked auto-encoder (SAE) for feature reduction to remove unnecessary data from breast cancer data. The model was assessed using Softmax, SVM, and the subspace-KNN classifiers.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Abdel-Naby

Alfonse

Roushdy

2021

Digital Transformation Technology

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Breast cancer is the most common cancer among women due to many factors such as heredity and unhealthy lifestyles. Early and accurate diagnosis of this cancer improves the patient quality of life and increases the survival rate. Microarray technology provides effective way to early diagnosis cancer. However, the nature of its data complicates the classification process. A hybrid approach of mutual information (MI), least absolute shrinkage and selection operator (LASSO) and genetic algorithm (GA) is proposed to face this challenge. The proposed approach is assessed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). Compared to the state-of-the-art models, the proposed approach can effectively diagnose the breast cancer with few numbers of genes. For the benchmarked Van't veer dataset, it obtained a classification accuracy of 96% with only 23 features. KeywordsCancer classification • Microarray data • Genetic algorithm • LASSO • Mutual information 1 Introduction Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. Early detection and diagnosis have been proven to significantly improve patient survival rates [1], quality of life, as well as to significantly reduce the cost and complexity of cancer treatment. Microarray technology has become benchmark technique for early cancer diagnosis [2]. It monitors each gene many times under different conditions or alternately evaluates each gene in M. Abd-elnaby (B) • M.

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“…RAUKT algoritması, K-EYK sınıflandırıcılarının sınıflandırma doğruluğunu artırmak için rastgele alt uzay topluluklarının kullanıldığı bir yöntemdir. Bu yöntemde, her alt uzaydaki sınıflandırıcı oluşturulurken öğrenme modeline bir dizi bileşenini rastgele seçen bir işlem uygulanmaktadır (Adem, 2020). Her sınıflandırıcıdaki eğitim veri seti rastgele alt uzaylara bölünmekte ve bu eğitim seti üzerinde test örnekleri kullanılarak Öklid ve Chebyshev gibi mesafe hesaplamaları yapılmaktadır.…”

Section: Raukt Ile Sınıflandırmaunclassified

Detection of Parkinson's Disease using Speech Signals with L1-Norm SVM and Chi-Square Based Feature Selection Algorithms

Demir

2021

International Journal of Pure and Applied Sciences

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Parkinson hastalığı, genel nöro-dejeneratif bozukluklardan biridir. Konuşma / ses bozukluğu Parkinson hastalığının erken dönemdeki semptomlarından biri olarak kabul edilir. Ses sinyallerini işleme yöntemleri, Parkinson hastalığı ile ilgili ses bozukluğunu potansiyel olarak değerlendirebilir ve ölçebilir. Bu çalışmada, Parkinson hastalığını konuşma sinyallerinden teşhis etmek için etkili bir makine öğrenmesi tekniği önerilmiştir. Önerilen yöntemde, sağlıklı kişilerin ve Parkinson hastalarının konuşma sinyallerinden çıkarılan öznitelikleri içeren bir veri seti kullanılmıştır. L1-Norm Destek Vektör Makinesi ve Ki-Kare Tabanlı öznitelik seçme algoritmaları kullanılarak veri setinde bulunan ayırt ediciliği yüksek öznitelikler seçilmiştir. İki yöntemden elde edilen öznitelik setleri birleştirilerek sınıflandırma aşamasında kullanılmıştır. Sınıflandırma aşamasında Destek Vektör Makinesi, K-En Yakın Komşu ve Rasgele Alt Uzay K-En Yakın Komşu Toplulukları sınıflandırıcılarının tahmin sonuçlarının kullanıldığı Çoğunluk Oylaması yöntemi ile önerilen yöntemin başarımı artırılmıştır. Önerilen yöntem %95.11 doğruluk ile aynı veri setini kullanan geçmiş çalışmalara göre daha iyi bir performans sağlamıştır. Önerilen yöntem ile Parkinson hastalığı otomatik olarak teşhis edileceğinden bu uygulama hekimlere karar verme süresinde yardımcı bir araç olarak kullanılabilir.

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Diagnosis of breast cancer with Stacked autoencoder and Subspace kNN

Cited by 34 publications

References 20 publications

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Detection of Parkinson's Disease using Speech Signals with L1-Norm SVM and Chi-Square Based Feature Selection Algorithms

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