Discriminant autoencoder for feature extraction in fault diagnosis

Luo, Xiaoyi; Li, Xianmin; Wang, Ziyang; Liang, Jun

doi:10.1016/j.chemolab.2019.103814

Cited by 40 publications

(17 citation statements)

References 16 publications

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“…Autoencoders are neural networks trained by unsupervised learning, which are trained to learn how to reconstruct data close to its original input ( Luo et al, 2019 ). The autoencoder consists of two parts, namely the encoder and the decoder, and its principle can be described as Eqs.…”

Section: Methodsmentioning

confidence: 99%

A novel method for credit scoring based on feature transformation and ensemble model

Feng

Lin

et al. 2021

PeerJ Computer Science

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Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

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Section: Methodsmentioning

confidence: 99%

A novel method for credit scoring based on feature transformation and ensemble model

Feng

Lin

et al. 2021

PeerJ Computer Science

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“…Bilindiği gibi sınıflandırıcının performansı, özniteliklerin kalitesine büyük ölçüde bağlıdır [30]. Kodlama işlemi, sınıflandırma, regresyon, bilgi görselleştirme gibi birçok farklı alanda sıklıkla kullanılmaktadır [31]. Bu çalışmada üç farklı kodlama tekniğinin (AE, VAE ve FD) görsel üretme üzerindeki performansları incelenmiştir.…”

Section: Kodlama Teknikleriunclassified

“…Çözücü ise giriş vektörü ile çıkış vektörü arasındaki farkı minimize etmeye çalışır. Yeniden yapılanma kaybı yeterince küçük olduğunda; kodlanmış ℎ vektörünün giriş vektörünü en iyi temsil ettiği düşünülür [31], [32]. Kodlayıcı modül aşağıdaki gibi ifade edilebilir:…”

Section: Otomatik Kodlayıcılar (Auto Encoder-ae)unclassified

Generating the image viewed from EEG signals

Çelik¹,

Talu²

2021

Pamukkale J Eng Sci

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EEG sinyalleri kullanılarak engelliler için kontrol edilebilir tekerlekli sandalyelerin üretildiği veya yapılması düşünülen aktivitenin tahmin edildiği çalışmalara literatürde sıklıkla rastlanmaktadır. Genel olarak bu çalışmalarda elektroensefalografi (EEG) sinyalinin önceden belirlenen sınıflara aktarımı gerçekleştirilir. Bu çalışmalar EEG sinyalinin sınıflandırmasından ibarettir. Ancak son yıllarda yapay öğrenme alanında yaşanan gelişmelerle sınıflandırmadan öteye gidildiği, EEG sinyalinden bakılan görselin üretilebildiği görülmektedir. Klasik çekişmeli üretici ağlar (Generative adversarial networks-GAN) ve otomatik kodlayıcı (Auto encoder-AE) yaklaşımlarının kullanıldığı sınırlı sayıdaki bu çalışmalar incelendiğinde, EEG sinyallerinden kabaca görsellerin üretilebildiği görülmektedir. Bu çalışmanın özgün yönü, görsel üretim kabiliyetini arttıracak matematiksel yaklaşımlar içermesidir. Klasik GAN mimarileri üretilen görüntülerin çeşitliliğini sağlayabilmek için rastgele vektör girişini kullanırlar. Bu yaklaşım ile EEG sinyalinden üretilen görsellerin düşük kalitede olduğu gözlemlenmiştir. Önerilen yöntemde giriş iki kısım (kodlanmış EEG ve rastgelelik) olarak düşünülmüştür. EEG'nin kodlanması için değişken oto kodlayıcı (Variational auto encoder-VAE) ve fourier dönüşümü (FD) kullanılırken, rastgelelik için iki farklı yaklaşım önerilmiştir. Bu özgün GAN kullanımı, EEG sinyallerinden daha kaliteli görsel üretilmesini sağlamıştır. Bu kalitenin sayısal olarak anlaşılabilmesi için önceden eğitilmiş evrişimsel sinir ağları (ESA) kullanılmıştır. Yapılan deneysel çalışmalar neticesinde, klasik GAN ile EEG'den üretilen görsellerin başarım seviyesi %93 civarındayken, önerilen yaklaşımda bu seviyenin %95-%100 aralığına çıktığı görülmektedir.In the literature, it is encountered a vast amount of studies related to the production of controllable wheelchairs for people with disabilities or the prediction of activity thought to be performed. In general, the electroencephalography (EEG) signal is transferred to predetermined classes in these studies. These studies consist of the classification of the EEG signal. However, it has been observed that in the recent years, with the developments in the field of artificial learning, the classification has gone beyond, It can be seen that the visual viewed from the EEG signal can be produced. When the limited number of studies using classical generative adversarial networks (GAN) and autu encoder (AE) approaches are examined, it is seen that visuals from EEG signals can be produced roughly. The original aspect of this study is that it includes mathematical approaches to increase the visual production capability. Classical GAN architectures use random vector input to provide a variety of images produced. With this approach, it is observed that the visuals produced from the EEG signal are of low quality. In the proposed method, the input is considered as two parts (coded EEG and randomness). Variable auto encoder (VAE) and fourier transform (FT) are used to encode the EEG, while two differ...

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“…The loss function used in training this network penalizes the input reconstruction error. After convergence, the trained network can be used for input reconstruction with minimal noise [ 45 ]. One of the advantages of an AE is that it can be used in learning a lower-dimensional representation of input data with low reconstruction error even when it spans a non-linear manifold in a feature space.…”

Section: Summary Of Deep Learning Techniques That Are Applied In Lmentioning

confidence: 99%

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

Alam

Al-Absi

Schmeier

2020

ncRNA

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Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome.

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Discriminant autoencoder for feature extraction in fault diagnosis

Cited by 40 publications

References 16 publications

A novel method for credit scoring based on feature transformation and ensemble model

A novel method for credit scoring based on feature transformation and ensemble model

Generating the image viewed from EEG signals

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

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