Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications最新文献_第2页

Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues. 利用2D生物工程组织的高通量数据预测发育神经毒性的机器学习。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2019-12-01 Epub Date: 2020-02-17 DOI: 10.1109/icmla.2019.00055

Finn Kuusisto, Vitor Santos Costa, Zhonggang Hou, James Thomson, David Page, Ron Stewart

{"title":"Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues.","authors":"Finn Kuusisto, Vitor Santos Costa, Zhonggang Hou, James Thomson, David Page, Ron Stewart","doi":"10.1109/icmla.2019.00055","DOIUrl":"https://doi.org/10.1109/icmla.2019.00055","url":null,"abstract":"There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. Prior work has demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2019 ","pages":"293-298"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icmla.2019.00055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37745667","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: 4

Evaluating the Performance of the Deep Active Imitation Learning Algorithm in the Dynamic Environment of FIFA Player Agents 深度主动模仿学习算法在FIFA球员代理动态环境中的性能评价

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00043

Matheus Prado Prandini Faria, Rita Maria Silva Julia, Lidia Bononi Paiva Tomaz

{"title":"Evaluating the Performance of the Deep Active Imitation Learning Algorithm in the Dynamic Environment of FIFA Player Agents","authors":"Matheus Prado Prandini Faria, Rita Maria Silva Julia, Lidia Bononi Paiva Tomaz","doi":"10.1109/ICMLA.2019.00043","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00043","url":null,"abstract":"Deep Learning is a state-of-the-art approach for machine learning using real-world or realist data. FIFA is a soccer simulation game that provides a very realistic environment, but which has been relatively poorly explored in the context of learned game-playing agents. This paper explores the Deep Active Imitation (DAI) learning strategy applied to a dynamic environment in FIFA game. DAI is a segment of Imitation Learning, which consists of a supervised Deep Learning training strategy where the agents learn by observing and replicating human experts' behavior. Noteworthy here is that such learning strategy has only been validated in static navigation scenarios in the sense that the environment is changed only through the actions of the agent. In this way, the main objective of the present work is to investigate the efficacy of DAI to cope with a dynamic FIFA scenario named confrontation mode. The agents were evaluated in terms of in-game score through tournaments against FIFA's engine. The results show that DAI performs well in the confrontation mode. Thus, this work indicates that such learning strategy can be used to solve complex problems.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"214 1","pages":"228-233"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77136849","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: 1

Two-Stage Machine Learning Framework for Simultaneous Forecasting of Price-Load in the Smart Grid 智能电网价格负荷同步预测的两阶段机器学习框架

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00176

T. Victoire, B. Gobu, S. Jaikumar, Arulmozhi Nagarajan, P. Kanimozhi, T. AmalrajVictoire

{"title":"Two-Stage Machine Learning Framework for Simultaneous Forecasting of Price-Load in the Smart Grid","authors":"T. Victoire, B. Gobu, S. Jaikumar, Arulmozhi Nagarajan, P. Kanimozhi, T. AmalrajVictoire","doi":"10.1109/ICMLA.2018.00176","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00176","url":null,"abstract":"In this paper, the electricity load and price patterns of consumers are forecasted using a two-stage forecasting framework. The electricity usage statistics of the consumers are recorded through smart meters and based on the historical load and price patterns the proposed model forecasts the future loads and prices used for further bidding purposes. A hybrid two stage forecasting framework combining the variational mode decomposition (VMD) method, echo state neural network (ESNN) and differential evolution (DE) algorithm is proposed. The training of the hybrid forecasting framework is done by decomposing the load and price time-series data using the VMD. The decomposed data are then used for training the ESNN. Differential evolution algorithm is used to tune the ESNN. Initially, the price and load data are used separately to train the ESNN, and in the second stage, both the data are used along with the forecasted output of the previous stage are used to train the ESNN. The proposed forecasting framework is experimented on 3 smart gird data derived from Smart Meter Energy Consumption Data in London Households of UK Power Networks (UKPN), for demonstration purpose.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"118 1","pages":"1081-1086"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73857514","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: 1

Elastic Time Series Motifs and Discords 弹性时间序列的主题和不和谐

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00042

Diego Furtado Silva, Gustavo E. A. P. A. Batista

{"title":"Elastic Time Series Motifs and Discords","authors":"Diego Furtado Silva, Gustavo E. A. P. A. Batista","doi":"10.1109/ICMLA.2018.00042","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00042","url":null,"abstract":"The recent proposal of the Matrix Profile (MP) has brought the attention of the time series community to the usefulness and versatility of the similarity joins. This primitive has numerous applications including the discovery of time series motifs and discords. However, the original MP algorithm has two prominent limitations: the algorithm only works for Euclidean distance (ED) and it is sensitive to the subsequences length. Is this work, we extend the MP algorithm to overcome both limitations. We use a recently proposed variant of Dynamic Time Warping (DTW), the Prefix and Suffix Invariant DTW (PSI-DTW) distance. The PSI-DTW allows invariance to warp and spurious endpoints caused by segmenting subsequences and has a side-effect of supporting the match of subsequences with different lengths. Besides, we propose a suite of simple methods to speed up the MP calculation, making it more than one order of magnitude faster than a straightforward implementation and providing an anytime feature. We show that using PSI-DTW avoids false positives and false dismissals commonly observed by applying ED, improving the time series motifs and discords discovery in several application domains.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"9 1","pages":"237-242"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84277235","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

Application of a Graphical Model to Investigate the Utility of Cross-channel Information for Mitigating Reverberation in Cochlear Implants. 应用图形模型研究人工耳蜗中跨通道信息对减轻混响的效用。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 Epub Date: 2019-01-17 DOI: 10.1109/ICMLA.2018.00136

Lidea K Shahidi, Leslie M Collins, Boyla O Mainsah

{"title":"Application of a Graphical Model to Investigate the Utility of Cross-channel Information for Mitigating Reverberation in Cochlear Implants.","authors":"Lidea K Shahidi, Leslie M Collins, Boyla O Mainsah","doi":"10.1109/ICMLA.2018.00136","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00136","url":null,"abstract":"Individuals with cochlear implants (CIs) experience more difficulty understanding speech in reverberant environ-ments than normal hearing listeners. As a result, recent research has targeted mitigating the effects of late reverberant signal reflections in CIs by using a machine learning approach to detect and delete affected segments in the CI stimulus pattern. Previous work has trained electrode-specific classification models to mitigate late reverberant signal reflections based on features extracted from only the acoustic activity within the electrode of interest. Since adjacent CI electrodes tend to be activated concurrently during speech, we hypothesized that incorporating additional information from the other electrode channels, termed cross-channel information, as features could improve classification performance. Cross-channel information extracted in real-world conditions will likely contain errors that will impact classification performance. To simulate extracting cross-channel information in realistic conditions, we developed a graphical model based on the Ising model to systematically introduce errors to specific types of cross-channel information. The Ising-like model allows us to add errors while maintaining the important geometric information contained in cross-channel information, which is due to the spectro-temporal structure of speech. Results suggest the potential utility of leveraging cross-channel information to improve the performance of the reverberation mitigation algorithm from the baseline channel-based features, even when the cross-channel information contains errors.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2018 ","pages":"847-852"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICMLA.2018.00136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37608407","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

Document Retrieval for Biomedical Question Answering with Neural Sentence Matching. 基于神经语句匹配的生物医学问答文档检索。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 Epub Date: 2019-01-17 DOI: 10.1109/ICMLA.2018.00036

Jiho Noh, Ramakanth Kavuluru

{"title":"Document Retrieval for Biomedical Question Answering with Neural Sentence Matching.","authors":"Jiho Noh, Ramakanth Kavuluru","doi":"10.1109/ICMLA.2018.00036","DOIUrl":"10.1109/ICMLA.2018.00036","url":null,"abstract":"Document retrieval (DR) forms an important component in end-to-end question-answering (QA) systems where particular answers are sought for well-formed questions. DR in the QA scenario is also useful by itself even without a more involved natural language processing component to extract exact answers from the retrieved documents. This latter step may simply be done by humans like in traditional search engines granted the retrieved documents contain the answer. In this paper, we take advantage of datasets made available through the BioASQ end-to-end QA shared task series and build an effective biomedical DR system that relies on relevant answer snippets in the BioASQ training datasets. At the core of our approach is a question-answer sentence matching neural network that learns a measure of relevance of a sentence to an input question in the form of a matching score. In addition to this matching score feature, we also exploit two auxiliary features for scoring document relevance: the name of the journal in which a document is published and the presence/absence of semantic relations (subject-predicate-object triples) in a candidate answer sentence connecting entities mentioned in the question. We rerank our baseline sequential dependence model scores using these three additional features weighted via adaptive random research and other learning-to-rank methods. Our full system placed 2nd in the final batch of Phase A (DR) of task B (QA) in BioASQ 2018. Our ablation experiments highlight the significance of the neural matching network component in the full system.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2018 ","pages":"194-201"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICMLA.2018.00036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36923970","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: 3

Distributed Primal-Dual Proximal Method for Regularized Empirical Risk Minimization 正则化经验风险最小化的分布原-对偶近端方法

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00152

M. B. Khuzani

引用次数: 0

Fuzzy Echo State Neural Network with Differential Evolution Framework for Time Series Forecasting 基于差分演化框架的模糊回声状态神经网络时间序列预测

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00214

D. Nachimuthu, S. Govindaraj, Anand Tirupur Shanmuganathan

{"title":"Fuzzy Echo State Neural Network with Differential Evolution Framework for Time Series Forecasting","authors":"D. Nachimuthu, S. Govindaraj, Anand Tirupur Shanmuganathan","doi":"10.1109/ICMLA.2018.00214","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00214","url":null,"abstract":"In this paper, differential evolution (DE) is used to find optimal weights for echo state neural network model and also to optimize the number of rules of the modeled fuzzy system that presents the input to the echo state neural network (ESNN) model. ESNN designed in this work possess a recurrent neuronal infra-structure called as reservoir. This work aims to develop a good reservoir for the ESNN model employing the coherent features and the ability of the differential evolution algorithm and fuzzy rule base system. DE aims to pre-train the fixed weight values of the network with its effective exploration and exploitation capability and fuzzy rule base system (FRBS) formulates a set of rules, which provides inferences for the inputs presented to the echo state network model. The performance of the developed optimized network is evaluated based on the error metrics and the computational time incurred for the training of the model. The test results of ESNN model using DE and FRBS are compared with that of ESNN without optimization and fuzzy rule to prove its validity and also with the related existing techniques. The perceived DE based fuzzy ESNN model is verified for its effectiveness with a set of time series forecasting benchmark problems. The empirical results prove the superiority and the effectiveness of the DE based fuzzy ESNN learning outcomes.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2 1","pages":"1322-1327"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74697714","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: 1

Evaluation of a New Kernel-Based Classifier in Eye Pupil Detection 一种新的基于核的瞳孔检测分类器的评价

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00063

P. Monforte, G. Araujo, A. Lima

{"title":"Evaluation of a New Kernel-Based Classifier in Eye Pupil Detection","authors":"P. Monforte, G. Araujo, A. Lima","doi":"10.1109/ICMLA.2018.00063","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00063","url":null,"abstract":"Accurate pupil location is paramount to applications such as gaze estimation, assistive technologies and several man-machine interfaces as the ones found in smartphones and VR applications. We introduce a new classifier stemmed from the Inner Product Detector and investigate its features on the challenging task of pupil localization. IPD (Inner Product Detector) is a classifier with high potential in facial landmarks detection. It is robust to variations in the desired pattern while maintaining good generalization and computational efficiency. However, one possible limitation is its linear behavior, which could be overcome by aggregating non-linear techniques, such as kernel methods. Although kernel classifiers have been exhaustively studied in the past two decades, it was not analyzed or applied with IPD, yet. The proposed KIPD achieves in the worst case an accuracy of 97.41% on the BioID dataset and 93.71% in LFPW dataset both at 10% of the interocular distance. In this paper the KIPD is compared to the state of the art methods, including the ones using deep learning, being competitive in terms of accuracy as well as computational complexity.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"76 1 1","pages":"380-385"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77263153","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

An Approximative Bayes-Optimal Kernel Classifier Based on Version Space Reduction 基于版本空间约简的近似贝叶斯最优核分类器

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00071

Karen Braga Enes, Saulo Moraes Villela, G. Pappa, R. F. Neto

{"title":"An Approximative Bayes-Optimal Kernel Classifier Based on Version Space Reduction","authors":"Karen Braga Enes, Saulo Moraes Villela, G. Pappa, R. F. Neto","doi":"10.1109/ICMLA.2018.00071","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00071","url":null,"abstract":"The Bayes-optimal classifier is defined as a classifier that induces an hypothesis able to minimize the prediction error for any given sample in binary classification problems. Finding the Bayes-optimal classifier is an intractable problem. It is known that it is approximately equivalent to the center of mass of the version space, which is given by the set of all classifiers consistent with the training set. Previously solutions to find the center of mass are not feasible, as they present a high computational cost, and do not work properly in non-linear separable problems. Aiming to solve these problems, this paper presents the Dual Version Space Reduction Machine (Dual VSRM), an effective kernel method to approximate the center of mass of the version space. The Dual VSRM algorithm employs successive reductions of the version space based on an oracle's decision. As an oracle, we propose the Ensemble of Dissimilar Balanced Kernel Perceptrons (EBPK). EBPK enhances the accuracy of each individual classifier by balancing the final hyperplane solution while maximizing the diversity of its components by applying a dissimilarity measure. In order to evaluate the proposed methods, we conduct an experimental evaluation on 7 datasets. We compare the performance of our proposed methods against several baselines. Our results for EBKP indicate the strategies for improving individual accuracy and diversity of the ensemble components work properly. Also, the Dual VSRM consistently outperforms the baselines, indicating that the proposed method generates a better approximation to the center of mass.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"23 1","pages":"436-442"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84616641","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