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检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

共 1949 条记录，以下是161-170 订阅

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An Unsupervised Feature learning Method for Enhancing the Generalization of Cancer Diagnosis 2021

An Unsupervised Feature Learning Method for Enhancing the Ge...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Liu, Zhen Wang, Ruoyu Zhang, Wenbin Tang, Deyu School of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou510006 China

ISBN: (纸本)9781450389310

machine learning techniques have been utilized on gene expression profiling for cancer diagnosis. However, the gene expression data suffer from the curse of high dimensionality. Different kinds of feature selection methods were proposed to decrease the features of specific cancer diagnosis. As the difficult of obtaining the samples of a particular tumor, the lack of training samples leads to the overfitting problem. To handle the two problems, this paper proposes an unsupervised feature learning method. This method is able to enhance the performance of unsupervised feature learning by leveraging the unlabeled samples from other sources. Since the method utilizes the knowledge among the expression data from different sources, it can boost cancer classification performance. The experimental results on the gene expression data proves that our method improves the generalization cancer diagnosis when the unlabeled data are used for unsupervised feature learning. © 2021 ACM.

关键词： Diseases

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Feature selection for big data based on mapreduce and voting mechanism 19

Feature selection for big data based on mapreduce and voting...

引用

19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Zhang, Su-Fang Zhai, Jun-Hai Tian, Shi Zhou, Xiang Li, Yan China Meteorological Administration Hebei Branch of China Meteorological Administration Training Center Baoding071000 China Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Baoding071002 China Beijing Normal University School of Applied Mathematics Zhuhai519087 China

ISBN: (纸本)9780738124261

With the rapid development of computer network technology and wireless sensor technology, as well as the arrival of the era of big data, the dimension and sample number of data are growing rapidly. Accordingly, it is important to investigate the problem of feature selection for big data and to design feature selection algorithm for big data. Based on MapReduce and voting mechanism, a feature selection method for big data is proposed in this paper. The proposed methods include three steps: Firstly, partition big data set into m subsets, and deploy the subsets to m computing nodes of Hadoop. Secondly, on the m computing nodes, we employ a feature selection algorithm based on genetic algorithm to select important features in parallel using local data subset, and obtain m feature subsets. Finally, for each feature, m feature subsets are used to vote on it, and the final feature subset is selected according to the voting results. Experimental results on four big data sets demonstrate that the proposed method is effective and efficient. © 2020 IEEE.

关键词： Genetic algorithms

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Semantic Auto-Encoder with L2-norm Constraint for Zero-Shot learning 2021

Semantic Auto-Encoder with L2-norm Constraint for Zero-Shot ...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Wu, Yuhao Cao, Weipeng Liu, Ye Ming, Zhong Li, Jianqiang Lu, Bo College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781450389310

Zero-Shot learning (ZSL) is an effective paradigm to solve label prediction when some classes have no training samples. In recent years, many ZSL algorithms have been proposed. Among them, semantic autoencoder (SAE) is widely used because of its simplicity and good generalization ability. However, our research found that most of the existing SAE based methods use implicit constraints to guarantee the mapping quality between feature space and semantic space. In fact, the implicit constraints are insufficient in minimizing the structural risk of the model and easy to cause the over-fitting problem. To solve this problem, we propose a novel SAE algorithm with the L2-norm constraint (SAE-L2) in this study. SAE-L2 adds the L2 regularization constraint to the mapping parameters in its optimization objective, which explicitly guarantees the structural risk minimization of the model. Extensive experiments on four benchmark datasets show that our proposed SAE-L2 can achieve better performance than the original SAE model and other ZSL algorithms. © 2021 ACM.

关键词： Semantics

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Applications of machine learning Techniques in Genetic Circuit Design 2021

Applications of Machine Learning Techniques in Genetic Circu...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Zhu, Jiajie Zhang, Qi Forouraghi, Babak Wang, Xiao Saint Joseph's University Department of Computer Science PhiladelphiaPA United States Arizona State University School of Biological and Health Systems Engineering TempeAZ United States

ISBN: (纸本)9781450389310

Construction of mathematical models to investigate genetic circuit design is a powerful technique in synthetic biology with real-world applications in biomanufacturing and biosensing. The challenge of building such models is to accurately discover flow of information in simple as well as complex biological systems. However, building synthetic biological models is often a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of various machine learning (ML) techniques to accurately construct mathematical models for predicting gene expressions in genetic circuit designs. Specifically, classification and regressions models were built using Random Forrest (RF), Support Vector machines (SVM), and Artificial Neural Networks (ANN). The obtained accuracy of the regression model using RF and ANN yielded R2 scores of 0.97 and 0.95, respectively, compared to the best score of 0.63 obtained in an earlier study. Furthermore, a classifier model was built using the green fluorescent protein (GFP) measurements obtained from the experiments conducted in this work. Biologists use GFP as an indicator of gene expression, enabling easy measurement of its protein level in the living cells. The measured GFP values were predicted with 100% accuracy by both RF and ANN classifier models while identifying various synthetic gene circuit patterns. The paper also highlights importance of the relevant data preparation techniques to ensure high accuracy is obtained by the utilized ML models. © 2021 ACM.

关键词： Gene expression

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Biological Named Entity Recognition and Role Labeling via Deep Multi-task learning 2021

Biological Named Entity Recognition and Role Labeling via De...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Deng, Fei Zhang, Dongdong Peng, Jing School of Computer Science and Technology Wuhan University of Technology Wuhan China

ISBN: (纸本)9781450389310

Bioscience is an experimental science. The qualitative and quantitative findings of the biological experiments are often exclusively available in the form of figures in published papers. In this paper, we introduce the SourceData model, which captures a key aspect of the biological experimental design by categorizing biological entity involved in the experiment into one of the six roles. Our work aims at determining whether a given entity is subjected to a perturbation or is the object of a measurement (entity role labeling) through automatic natural language algorithms. We use state-of-the-art transformer models (e.g., Bert and its variants) as a strong baseline, find that after jointly trained with biological named entity recognition task by deep multi-task learning (MTL), the F1 score gets improved by 2% compared to previous single-task architecture. Also, for named entity recognition task, the MTL method achieves comparable performance in five public datasets. Further analysis reveals the importance of fusing entity information at the input layer of entity role labeling task and incorporating global context. © 2021 ACM.

关键词： Linearization

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Online Optimal Investment Portfolio Model Based on Deep Reinforcement learning 2021

Online Optimal Investment Portfolio Model Based on Deep Rein...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Gao, Ni He, Yiyue Jiao, Yuming Chang, Zhuo School of Economics and Finance Xi'an International Studies University Xi'an710128 China School of Economics and Management Northwest University Xi'an710127 China

ISBN: (纸本)9781450389310

Combining deep reinforcement learning with portfolio management is one of the research hotspots in the field of quantitative investment. In recent years, quantitative investment has become one of the research hotspots in the field of deep reinforcement learning. Combining portfolio management with deep reinforcement learning has become a hot research direction. The model in this paper combines the perception ability of deep learning and the online decision-making ability of reinforcement learning to construct an intelligent model of portfolio management based on deep reinforcement learning DDPG algorithm. This model addresses issues such as the limited ability of reinforcement learning models to perceive complex environments and the strong correlation between the internal features of state data, introducing the convolutional neural network into the DDPG model. The use of convolutional neural networks to process multi-dimensional state data improves the perception ability of the reinforcement learning model and solves the problem of data correlation. Therefore, the model in this paper can obtain the dynamic optimal investment portfolio within the specified trading time range. At the same time, this paper introduces the action vector into the state value representing the stock market environment, and solves the problem of static model. Experiments show that our model improves the income acquisition ability of the portfolio management model. © 2021 ACM.

关键词： Convolutional neural networks

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Bert-pair-networks for sentiment classification 19

Bert-pair-networks for sentiment classification

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Wang, Ziwen Wu, Haiming Liu, H.A.N. Cai, Qian-Hua Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou510006 China South China Normal University School of Physics and Telecommunication Engineering Guangzhou510006 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9780738124261

BERT has demonstrated excellent performance in natural language processing due to the training on large amounts of text corpus in an unsupervised way. However, this model is trained to predict the next sentence, and thus it is good at dealing with sentence pair tasks but may not be sufficiently good for other tasks. In our paper, we introduce a novel representation framework BERT-pair-Networks (p-BERTs) for sentiment classification, where p-BERTs involve adopting BERT to encode sentences for sentiment classification as a classic task of single sentence classification, using the auxiliary sentence, and a feature extraction layer on the top. Results on three datasets show that our method achieves considerably improved performance. © 2020 IEEE.

关键词： Natural language processing systems

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Mini-projects based Cybersecurity Modules for an Operating System Course using xv6 129

Mini-projects based Cybersecurity Modules for an Operating S...

引用

129th ASEE Annual conference and Exposition: Excellence Through Diversity, ASEE 2022

作者： Tan, Jansen Ravindra, Divya Niyaz, Quamar Yang, Xiaoli Javaid, Ahmad Y. Paheding, Sidike ECE Department Purdue University Northwest HammondIN46323 United States CSE Department Fairfield University FairfieldCT06824 United States EECS Department The University of Toledo ToledoOH43606 United States Applied Computing Michigan Technological University HoughtonMI49931 United States

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Generalized Intersection over Union Based Online Weighted Multiple Instance learning Algorithm for Object Tracking 2021

Generalized Intersection over Union Based Online Weighted Mu...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Lu, Xiaoshun Chen, Si Zheng, Zhuoyuan Weng, Chenyu Xu, Rui School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450389310

The traditional weighted multiple instance learning based online object tracking methods often use the Euclidean distance between the centers of the bounding boxes of the target and the instance to weight the instances in the positive bag, which can not effectively measure the contribution degree of the instances of the positive and negative bags and easily causes the object drifting problem. This paper proposes a generalized intersection over union based online weighted multiple instance learning algorithm (named GIoU-WMIL) for object tracking. This algorithm introduces a novel generalized intersection over union (GIoU) to calculate the overlap degree between the bounding boxes of the target and each instance in the bags, in order to effectively measure the contribution of the different instances. Furthermore, a new objective function is designed by employing the GIoU-based weights of all the instances in the positive and negative bags. Experiments show that the proposed algorithm has the good robustness and accuracy on several challenging video sequences. © 2021 ACM.

关键词： learning algorithms

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Supervised neighborhood based ensemble attribute reduction 19

Supervised neighborhood based ensemble attribute reduction

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19th international conference on machine learning and Cybernetics, icmlc 2020

作者： Song, Jingjing Jiang, Zehua Dou, Huili Tsang, Eric C. C. Jiangsu University of Science and Technology School of Computer Jiangsu Province Zhenjiang212003 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou363000 China Macau University of Science and Technology Faculty of Information Technology China

ISBN: (纸本)9780738124261

In neighborhood based attribute reduction, neighborhood relation is a typical tool for distinguishing samples. Notably, the neighborhood relation may be powerless in providing satisfactory distinguishing ability. In view of this, the supervised neighborhood based attribute reduction has been explored. However, the supervised neighborhood based reduct may be lack of universality. To file such gap, an ensemble strategy for computing supervised neighborhood based reduct is proposed in our paper. Such ensemble strategy is realized through considering the requirement of each decision class. The experimental results on 8 UCI data sets show that the supervised neighborhood based ensemble strategy can generate reduct not only with higher generalization performance but also with higher stability. © 2020 IEEE.

关键词： machine learning Tools Cybernetics

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