2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第7页

Event Prioritization and Correlation Based on Pattern Mining Techniques 基于模式挖掘技术的事件优先级和相关性

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.76

Mona Lange, Ralf Möller, G. Lang, Felix Kuhr

{"title":"Event Prioritization and Correlation Based on Pattern Mining Techniques","authors":"Mona Lange, Ralf Möller, G. Lang, Felix Kuhr","doi":"10.1109/ICMLA.2015.76","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.76","url":null,"abstract":"With the growing deployment of host and network intrusion detection systems in increasingly large and complex communication networks, managing low-level events from these systems becomes critically important. A network has multiple tasks, which consist of multiple network services aiding the execution of a task. An emerging track of security research has focused on event prioritization and correlation to rank the criticality of events and reduce the number of low-level events. To prioritize and correlate events, the ongoing tasks in an enterprise network are identified, as the goal of network operators is to protect ongoing tasks when a security breach occurs. The prioritization of an event depends on the criticality of an ongoing task that is potentially threatened by the event. Additionally, in order to support network operators, we correlate all events that target the same task. A particular task may depend on multiple network services and involve multiple network devices. So, if one network service becomes unavailable, other network services will be affected over time since they Unfortunately, dependency details are often not documented and are difficult to discover by relying on human expert knowledge. In order to solve this problem, a network dependency analysis based on network traffic is conducted. We rely on pattern mining techniques to discover tasks in a monitored enterprise network. A formal description of the identified tasks is provided and events are prioritized and correlated based on this model. The pattern mining based network dependency analysis algorithm is evaluated based on a real-world network and three networks that where created with a network simulator.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129140184","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

Integrating Active Learning with Supervision for Crowdsourcing Generalization 整合主动学习与监督的众包泛化

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.13

Zhenyu Shu, V. Sheng, Yang Zhang, Dianhong Wang, J. Zhang, Heng Chen

{"title":"Integrating Active Learning with Supervision for Crowdsourcing Generalization","authors":"Zhenyu Shu, V. Sheng, Yang Zhang, Dianhong Wang, J. Zhang, Heng Chen","doi":"10.1109/ICMLA.2015.13","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.13","url":null,"abstract":"With various online crowdsourcing platforms, it is easy to collect multiple labels for the same examples from the crowd. Consensus integration algorithms can infer the estimated ground truths from the multiple label sets of these crowdsourcing datasets. However, it couldn't be avoided that these integrated (estimated) labels still contain noises. In order to further improve the performance of a model learned from data with these integrated labels, we propose an active learning framework to further improve the data quality, such that to improve the model quality, through acquiring limited true labels from experts (the oracle). We further investigate two active learning strategies in terms of two uncertainty measures (i.e., CLUE and MUE) within the active learning framework. From our experimental results on eight simulation crowdsourcing datasets and four real-world crowdsourcing datasets with three popular consensus integration algorithms, we draw several conclusions as follows. (i) Our active learning framework with the input from the oracle significantly improves the generalization ability of the model learned from crowdsourcing data. (ii) Our two active learning strategies outperform a random active learning strategy.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"35 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114101326","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: 5

Transfer Learning of Air Combat Behavior 空战行为的迁移学习

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.61

A. Toubman, J. Roessingh, P. Spronck, A. Plaat, Jaap van den Herik

引用次数: 12

Multi-label Classification of Anemia Patients 贫血患者的多标签分类

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.112

C. Bellinger, A. Amid, N. Japkowicz, H. Viktor

引用次数: 13

ABC-sampling for Balancing Imbalanced Datasets Based on Artificial Bee Colony Algorithm 基于人工蜂群算法的abc采样失衡数据集平衡

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.103

Ali Braytee, F. Hussain, Ali Anaissi, Paul J. Kennedy

引用次数: 9

Example-Specific Density Based Matching Kernels for Scene Classification Using Support Vector Machines 使用支持向量机进行场景分类的基于特定密度的匹配核

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.162

Abhijeet Sachdev, Veena Thenkanidiyoor, A. D. Dileep, C. Sekhar

{"title":"Example-Specific Density Based Matching Kernels for Scene Classification Using Support Vector Machines","authors":"Abhijeet Sachdev, Veena Thenkanidiyoor, A. D. Dileep, C. Sekhar","doi":"10.1109/ICMLA.2015.162","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.162","url":null,"abstract":"In this paper, we propose the example-specific density based matching kernel (ESDMK) for classification of scene images represented as sets of local feature vectors. The proposed kernel is computed between the pair of examples, represented as sets of local feature vectors, by matching the estimates of example-specific densities computed at every local feature vector in those two examples. In this work, the number of local feature vectors of an example among the K nearest neighbors of a local feature vector is considered as an estimate of the example-specific density. The minimum of the two example-specific densities, one for each example, at a local feature vector is considered as the matching score. The ESDMK is then computed as the sum of the matching score computed at every local feature vector in a pair of examples. We also propose the spatial ESDMK (SESDMK) to include spatial information present in the scene images while matching the pair of scene images. Each of the scene images is divided spatially into a fixed number of regions. Then the SESDMK is computed as a combination of region specific ESDMKs that match the corresponding regions. We study the performance of the support vector machine (SVM) based classifiers using the proposed ESDMKs for scene classification and compare with that of the SVM-based classifiers using the state-of-the-art kernels for sets of local feature vectors.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123669761","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

Achievements Recommendation Framework Based on Scientific Collaboration Network 基于科学协作网络的成果推荐框架

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.182

Xiaohui Li, Jie Peng, Shanqing Li

引用次数: 1

A Study of the Use of Complexity Measures in the Similarity Search Process Adopted by kNN Algorithm for Time Series Prediction kNN算法用于时间序列预测的相似性搜索过程中复杂度度量的应用研究

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.217

A. R. Parmezan, Gustavo E. A. P. A. Batista

{"title":"A Study of the Use of Complexity Measures in the Similarity Search Process Adopted by kNN Algorithm for Time Series Prediction","authors":"A. R. Parmezan, Gustavo E. A. P. A. Batista","doi":"10.1109/ICMLA.2015.217","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.217","url":null,"abstract":"In the last two decades, with the rise of the Data Mining process, there is an increasing interest in the adaptation of Machine Learning methods to support Time Series non-parametric modeling and prediction. The non-parametric temporal data modeling can be performed according to local and global approaches. The most of the local prediction data strategies are based on the k-Nearest Neighbor (kNN) learning method. In this paper we propose a modification of the kNN algorithm for Time Series prediction. Our proposal differs from the literature by incorporating three techniques for obtaining amplitude and offset invariance, complexity invariance, and treatment of trivial matches. We evaluate the proposed method with six complexity measures, in order to verify the impact of these measures in the projection of the future values. Besides, we face our method with two Machine Learning regression algorithms. The experimental comparisons were performed using 55 data sets, which are available at the ICMC-USP Time Series Prediction Repository. Our results indicate that the developed method is competitive and the use of a complexity-invariant distance measure generally improves the predictive performance.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131476466","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: 19

Mining over a Reliable Evidential Database: Application on Amphiphilic Chemical Database 挖掘可靠的证据数据库:在两亲性化学数据库中的应用

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.31

Ahmed Samet, T. Dao

引用次数: 3

Adaptive Fuzzy Prediction for Automotive Applications Usage 汽车应用中的自适应模糊预测

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.138

Shiqi Qiu, R. McGee, Y. L. Murphey

引用次数: 4