Transactions on machine learning research最新文献_第5页

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts. 你的公平性有多强?看不见的分配变化下的公平评估与维持。

Transactions on machine learning research Pub Date : 2023-01-01

Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang

引用次数: 0

Estimating Potential Outcome Distributions with Collaborating Causal Networks. 利用协作因果网络估算潜在结果分布。

Transactions on machine learning research Pub Date : 2022-09-01

Tianhui Zhou, William E Carson, David Carlson

{"title":"Estimating Potential Outcome Distributions with Collaborating Causal Networks.","authors":"Tianhui Zhou, William E Carson, David Carlson","doi":"","DOIUrl":"","url":null,"abstract":"Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the full potential outcome distributions. Estimation of outcome distributions via the CCN framework does not require restrictive assumptions of the underlying data generating process (e.g. Gaussian errors). Additionally, our proposed method facilitates estimation of the utility of each possible treatment and permits individual-specific variation through utility functions (e.g. risk tolerance variability). CCN not only extends outcome estimation beyond traditional risk difference, but also enables a more comprehensive decision making process through definition of flexible comparisons. Under assumptions commonly made in the causal inference literature, we show that CCN learns distributions that asymptotically capture the correct potential outcome distributions. Furthermore, we propose an adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational studies. Finally, we evaluate the performance of CCN in multiple experiments on both synthetic and semi-synthetic data. We demonstrate that CCN learns improved distribution estimates compared to existing Bayesian and deep generative methods as well as improved decisions with respects to a variety of utility functions.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10769464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts 你的公平性有多强?看不见的分配变化下的公平评估与维持

Transactions on machine learning research Pub Date : 2022-07-04 DOI: 10.48550/arXiv.2207.01168

Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang

引用次数: 5

Design to Build E-learning Application in SMP N 2 Busalangga Busalangga中学电子学习应用的设计

Transactions on machine learning research Pub Date : 2021-01-01 DOI: 10.11648/j.mlr.20210602.11

Jimi Asmara, Gregorius Rinduh Iriane, Edwin Ariesto Umbu Malahina

引用次数: 0

A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression 临床抑郁症的遗传神经模糊诊断系统

Transactions on machine learning research Pub Date : 2021-01-01 DOI: 10.11648/j.mlr.20210602.12

A. Adegboye, Imianvan Anthony Agboizebeta

引用次数: 2

Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset 使用小数据集的深度学习网络为波斯语用户设计语音控制轮椅

Transactions on machine learning research Pub Date : 2021-01-01 DOI: 10.11648/j.mlr.20210601.11

Masoud Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh

{"title":"Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset","authors":"Masoud Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh","doi":"10.11648/j.mlr.20210601.11","DOIUrl":"https://doi.org/10.11648/j.mlr.20210601.11","url":null,"abstract":": With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74669781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signed Product Cordial of the Sum and Union of Two Fourth Power of Paths and Cycles 路径与环的两次四次幂和并的签名积

Transactions on machine learning research Pub Date : 2019-12-30 DOI: 10.11648/J.MLR.20190404.11

S. Nada, A. Elrayes, A. Elrokh, A. Rabie

引用次数: 0

Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study 利用人工神经网络预测糖尿病的仿真研究

Transactions on machine learning research Pub Date : 2019-09-02 DOI: 10.11648/J.MLR.20190402.12

S. U. Gulumbe, S. Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, U. Usman

{"title":"Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study","authors":"S. U. Gulumbe, S. Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, U. Usman","doi":"10.11648/J.MLR.20190402.12","DOIUrl":"https://doi.org/10.11648/J.MLR.20190402.12","url":null,"abstract":"Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81863464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Learning Algorithms Using BPNN & SFS for Prediction of Compressive Strength of Ultra-High Performance Concrete 基于BPNN和SFS的超高性能混凝土抗压强度预测学习算法

Transactions on machine learning research Pub Date : 2019-06-25 DOI: 10.11648/J.MLR.20190402.11

Deepak Choudhary

{"title":"Learning Algorithms Using BPNN & SFS for Prediction of Compressive Strength of Ultra-High Performance Concrete","authors":"Deepak Choudhary","doi":"10.11648/J.MLR.20190402.11","DOIUrl":"https://doi.org/10.11648/J.MLR.20190402.11","url":null,"abstract":"This paper presents machine learning algorithms based on back-propagation neural network (BPNN) that employs sequential feature selection (SFS) for predicting the compressive strength of Ultra-High Performance Concrete (UHPC). A database, containing 110 points and eight material constituents, was collected from the literature for the development of models using machine learning techniques. The BPNN and SFS were used interchangeably to identify the relevant features that contributed with the response variable. As a result, the BPNN with the selected features was able to interpret more accurate results (r = 0.991) than the model with all the features (r2 = 0.816). The utilization of ANN modelling made its way into the prediction of fresh and hardened properties of concrete based on given experimental input parameters, whereby several authors developed AI models to predict the compressive strength of normal weight, light weight and recycled concrete. The steps that were are followed in developing a robust and accurate numerical model using SFS include (1) design and validation of ANN model by manipulating the number of neurons and hidden layers; (2) execution of SFS using ANN as a wrapper; and (3) analysis of selected features using both ANN and nonlinear regression. It is concluded that the usage of ANN with SFS provided an improvement to the prediction model’s accuracy, making it a viable tool for machine learning approaches in civil engineering case studies.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88290155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

QoS Aware Cloud Based Routing Protocol for Security Improvement of Hybrid Wireless Network 基于QoS感知的云路由协议提高混合无线网络的安全性

Transactions on machine learning research Pub Date : 2019-06-21 DOI: 10.11648/J.MLR.20190401.14

U. K. Thakur, Chandrashekhar Dethe

{"title":"QoS Aware Cloud Based Routing Protocol for Security Improvement of Hybrid Wireless Network","authors":"U. K. Thakur, Chandrashekhar Dethe","doi":"10.11648/J.MLR.20190401.14","DOIUrl":"https://doi.org/10.11648/J.MLR.20190401.14","url":null,"abstract":"The recent advances and the convergence of micro electro-mechanical systems technology, integrated circuit technologies, microprocessor hardware and Nano-technology, wireless communications, Ad-hoc networking routing protocols, distributed signal processing, and embedded systems have made the concept of Wireless Sensor Networks (WSNs). Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. Even though sensor networks are primarily designed for monitoring and reporting events, since they are application dependent, a single routing protocol cannot be efficient for sensor networks across all applications. In this paper, we analyze the design issues of sensor networks and present a classification and comparison of routing protocols. This comparison reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols for sensor networks. A reliable transmission of packet data information, with low latency and high energy-efficiency, is truly essential for wireless sensor networks, employed in delay sensitive industrial control applications. The proper selection of the routing protocol to achieve maximum efficiency is a challenging task, since latency, reliability and energy consumption are inter-related with each other. It is observed that, Quality of Service (QoS) of the network can improve by minimizing delay in packet delivery, and life time of the network, can be extend by using suitable energy efficient routing protocol.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84464562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4