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Feature selection methods on biological knowledge discovery and data mining: A survey 25

Feature selection methods on biological knowledge discovery ...

引用

25th international workshop on database and Expert Systems Applications, DEXA 2014

作者： Mhamdi, Hanen Mhamdi, Faouzi Tunis Tunisia

ISBN: (纸本)9781479957224

Feature selection is an important component of data mining and knowledge discovery process, due to the availability of data with hundreds of variables leading to data with very high dimension. It aims at reducing the number of features by removing irrelevant or redundant ones, while trying to reduce computation time, preserve or improve prediction performance, and to a better understanding of the data in machine learning or pattern recognition and specific in bioinformatics applications where the number of features is significantly larger than the number of samples. In this paper we provide an overview of some feature selection methods present in literature. We focus on Filter, Wrapper and hybrid methods. We also apply some of the feature selection techniques on standard databank to demonstrate their applicability. © 2014 IEEE.

关键词： Feature Selection

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Feature learning by Least Generalization 1

引用

30th international Conference on Inductive Logic Programming (ILP) held as part of the 1st international Joint Conference on learning and Reasoning (IJCLR)

作者： Nguyen, Hien D. Sakama, Chiaki Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Wakayama Univ Wakayama Japan

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least generalization can extract a common pattern of input data. We introduce three different methods for feature extraction based on symbolic manipulation. We perform experiments using the MNIst datasets and show that the proposed methods successfully capture features from training data and classify test data in around 90% accuracies. The results of this paper show potentials of induction and symbolic reasoning to feature learning or pattern recognition from raw data.

关键词： Feature learning Least generalization Interpretable machine learning

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Fusion of Visual and Anamnestic data for the Classification of Skin Lesions with Deep learning 2nd

Fusion of Visual and Anamnestic Data for the Classification ...

引用

2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Bonechi, Simone Bianchini, Monica Bongini, Pietro Ciano, Giorgio Giacomini, Giorgia Rosai, Riccardo Tognetti, Linda Rossi, Alberto Andreini, Paolo Department of Information Engineering and Matemathics University of Siena Siena Italy Department of Biotechnology Chemistry and Pharmacy University of Siena Siena Italy Department of Medicine Surgery and Neuroscience University of Siena Siena Italy Department of Medical Biotechnologies University of Siena Siena Italy Department of Information Engineering University of Florence Florence Italy

ISBN: (纸本)9783030307530

Early diagnosis of skin lesions is essential for the positive outcome of the disease, which can only be resolved with surgical treatment. In this manuscript, a deep learning method is proposed for the classification of cutaneous lesions based on their visual appearance and on the patient’s anamnestic data. These include age and gender of the patient and position of the lesion. The classifier discriminates between benign and malignant lesions, mimicking a typical procedure in dermatological diagnostics. Good preliminary results on the ISIC dataset demonstrate the importance of the information fusion process, which significantly improves the classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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pattern recognition using statistical and neural techniques

Pattern recognition using statistical and neural techniques

引用

1st international Conference on Computer Networks and Information Technology, ICCNIT'11

作者： Ahmad, Tasweer Jameel, Ahlam Ahmad, Balal Government College University Lahore Pakistan

ISBN: (纸本)9781612849416

pattern recognition has become an attractive research oriented field of the computer vision and machine learning for the last few decades. Neural pattern recognition techniques are also being exercised for pattern recognition, showing promising results. In this paper, a comparison is made between statistical and neural pattern recognition techniques and tried to realize how neural techniques reveal far better results than statistical techniques. In this comparison, Discriminant Analysis (DA) and Principal Component Analysis (PCA) are used for pattern recognition, which are a statistical technique. Discriminant Analysis engrosses the problem of huge data dimensions and small sample size. To evade these problems, pattern recognition task is also implemented using Generalized Regression Neural Network (GRNN) and Back-propagation Neural Network (BPNN) techniques. The task of pattern recognition is conceded on a data base of face images of 400 people. Neural networks proved results for better than statistical methods. © 2011 IEEE.

关键词： Discriminant analysis

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A Comparative Investigation on the use of machine learning Techniques for Currency Authentication 1

A Comparative Investigation on the use of Machine Learning T...

引用

1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Sharma, Arpit Prathap, Boppuru Rudra Hussain, Javid Computer Science and Engineering Bangalore India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

In the present banking sector, identifying the real and the fake note is a very challenging task because if we do it manually, it takes a long time to check which is real and which is fake. This research study article aims to authenticate the money between real and fake by using different machine algorithms facilitating learning, such as K-means Clustering, Random Forest Classification, Support Vector machines, and logistics Regression. Specifically, we consider the banknote dataset. The data of money is extracted from various banknote images by using the wavelet transform tool, which is primarily used to remove elements from the images. However, we are mainly concerned with the different machine learning algorithms, so we take the two variables, where the first variable indicates image variance and the second indicates image skewness. We use these two variables to train our machine learning algorithms. So, majorly, by applying the different machine learning algorithms, which are supervised and unsupervised, we find the accuracy for the respective machine learning algorithms and then visualize and classify the real and fake notes separately. Finally, the prediction is based on integrity, which means the efficiency value is based on how much the mechanism system can uncover the fake notes. Then, after calculating the accuracy of currency authentication, there is a high possibility that the accuracy of the particular algorithm is the best algorithm, so the application of currency authentication will be very useful for the bank to easily find duplicate notes. © 2022 IEEE.

关键词： K-means clustering

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Improving Ancient Cham Glyph recognition from Cham Inscription Images Using data Augmentation and Transfer learning 2nd

Improving Ancient Cham Glyph Recognition from Cham Inscripti...

引用

作者： Nguyen, Minh-Thang Schweyer, Anne-Valérie Le, Thi-Lan Tran, Thanh-Hai Vu, Hai Computer Vision Department MICA International Research Institute Hanoi University of Science and Technology Hanoi Viet Nam French National Center for Scientific Research Paris France

ISBN: (纸本)9783030307530

Ancient Cham glyphs have mostly appeared in inscriptions on stones at some museums in Vietnam. Unfortunately, these inscriptions are being abrasive by the time. To conserve Cham heritage as well as to make them widely accessible and readable by users, digitization and recognition of ancient Cham glyphs become necessary. In our previous work, we have built the first dataset of champ inscription images, manually segmented them in glyphs and annotated by an ancient Cham expert. We adapted some automatic recognition methods and conducted experiments on the manually denoised dataset. The aim of this paper is to extend that earlier research to work on noising data. To this end, we face two main issues. Firstly, the current pre-built dataset is still small which is usually a main drawback for deep learning based methods. Therefore, some data augmentation techniques will be evaluated and investigated to increase the number and variation of samples in the dataset. Second, even with the augmented dataset, the fact of training a deep model from scratch could be very long and sometimes cannot meet a good local minimum. Therefore, we use a simple transfer learning procedure which inherits knowledge from similar or of the same family language. Experiments on both the raw test set and its denoised version show very promising results and of F1-score on two test sets respectively). © 2019, Springer Nature Switzerland AG.

关键词： Image classification

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Progressive Review Towards Deep learning Techniques 1st

Progressive Review Towards Deep Learning Techniques

引用

1st international Conference on data Engineering and Communication Technology (ICDECT)

作者： Chaudhari, Poonam Agarwal, Himanshu Symbiosis Inst Technol Pune Maharashtra India

ISBN: (纸本)9789811016752;9789811016745

Deep learning is a thing of tomorrow which is causing a complete drift from shallow architecture to deep architecture and an estimate shows that by 2017 about 10 % of computers will be learning rather than processing. Deep learning has fast growing effects in the area of pattern recognition, computer vision, speech recognition, feature extraction, language processing, bioinformatics, and statistical classification. To make a system learn, deep learning makes use of a wide horizon of machine learning algorithms. Gene expression data is uncertain and imprecise. In this paper, we discuss supervised and unsupervised algorithms applied to gene expression dataset. There are intermediate algorithms classified as semi-supervised and self taught which also play an important role to improve the prediction accuracy in diagnosis of cancer. We discuss deep learning algorithms which provide better analysis of hidden patterns in the dataset, thus improving the prediction accuracy.

关键词： Deep learning Supervised and unsupervised algorithm Semi-supervised and self-taught algorithm Gene expression data

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Applications of Concurrent Sequential patterns in Protein data mining

Applications of Concurrent Sequential Patterns in Protein Da...

引用

10th international Conference on machine learning and data mining (MLDM)

作者： Wang, Cuiqing Lu, Jing Keech, Malcolm Shenyang Univ Chem Technol Shenyang 110142 Peoples R China Southampton Solent Univ Southampton SO14 0YN Hants England Univ Bedfordshire Luton LU1 3JU Beds England

ISBN: (纸本)9783319089799;9783319089782

Protein sequences of the same family typically share common patterns which imply their structural function and biological relationship. Traditional sequential patterns mining has its focus on mining frequently occurring sub-sequences. However, a number of applications motivate the search for more structured patterns, such as protein motif mining. This paper builds on the original idea of structural relation patterns and applies the Concurrent Sequential patterns (ConSP) mining approach in bioinformatics. Specifically, a new method and algorithms are presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Experiments with real-world protein datasets highlight the applicability of the ConSP methodology in protein data mining. The results show the potential for knowledge discovery in the field of protein structure identification.

关键词： protein sequences data mining concurrent sequential patterns (ConSP) bioinformatics ConSP mining concurrent vector method PROSITE knowledge discovery

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Local learning framework for recognition of lowercase handwritten characters

引用

2nd international workshop on machine learning and data mining in pattern recognition, MLDM 2001

作者： Dong, Jian-Xiong Krzyżak, Adam Suen, C.Y. Centre Of Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada Department of Computer Science Concordia University 1455 de Maisonneuve Blvd. W MontrealQCH3G 1M8 Canada

ISBN: (纸本)3540423591

This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method. The learning framework consists of quantization layer and ensemble layer. After GLVQ and MLP are applied to the framework, the proposed method is tested on public handwritten lowercase data sets, which obtains a promising performance consistently. Further, in contrast to LeNet5, an effective neural network structure, our method is especially suitable for a large-scale real-world classification problem although it is easily scaled to a small training set with preserving a good performance. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Classification (of information)

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Self-supervised Low-light Image Enhancement Based on Retinex and Histogram Equalization Prior 24

Self-supervised Low-light Image Enhancement Based on Retinex...

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Kong, Taoru Rey, William P. Mapua Univ Sch Informat Technol Manila Philippines

ISBN: (纸本)9798400707032

Images taken in low light often exhibit low visibility, high noise, and uneven lighting distribution, severely limit the application of images in fields such as object detection, image recognition, satellite and aerial imaging. Currently, deep learning-based low-light image enhancement(LLIE) methods have proven to be notably effective. However, the need for a vast collection of genuine paired data restricts the universality of these models. This study introduces a novel self-supervised framework named SID-RetinexHEPNet, which consists of three parts: a Retinex-based self-supervised image decomposition network (RetinexHEPNet), a non-linear illumination enhancement function (NIEF), and an image enhancement module (IEM). Operating as a self-supervised model, SID-RetinexHEPNet performs a direct decomposition of the low-light image into reflectance, illumination, and noise components, without the use of pre-training or reference images. Subsequently, the decomposed illumination map is enhanced through NIEF. Finally, IEM performs image enhancement on the low-light image using the enhanced illumination map. Experiments demonstrate that the images enhanced by this method not only exhibit richer textures and brightness but also effectively avoid over-enhancement or insufficient detail enhancement.

关键词： Low-light image enhancement Self-supervised learning Histogram equalization prior Channel attention

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