2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)最新文献

{"title":"A new energy efficient cluster-head and backup selection scheme in WSN","authors":"D. Izadi, J. Abawajy, Sara Ghanavati","doi":"10.1109/IRI.2013.6642500","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642500","url":null,"abstract":"Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123145610","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}

{"title":"Problem decomposition and sub-model reconciliation of control systems in Event-B","authors":"Sanaz Yeganefard, M. Butler","doi":"10.1109/IRI.2013.6642515","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642515","url":null,"abstract":"To break the complexity of the formalisation process, we propose to model a functional requirement document of a control system as composeable monitored, controlled, mode and commanded sub-models. Influenced by the problem frame approach and the decomposition of the four-variable model, we suggest decomposing requirements of a control system into monitored, controlled, mode and commanded sub-problems. Each sub-problem can be formalised in a step-wise manner as a separate sub-model. To introduce the phenomena shared amongst the subproblems, the sub-models are reconciled. We propose a reconciliation process in the Event-B formal language based on the shared-variable and the shared-event styles which were originally developed for a model decomposition. The advantages and disadvantages of shared-variable and the shared-event reconciliation steps are also discussed. The requirements of an automotive cruise control system are decomposed and formalised as sub-models. These sub-models are also reconciled to introduce shared phenomena.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131535893","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}

{"title":"Parse tree based approach for processing XML streams","authors":"M. Khabbaz, Dirar Assi, R. Alhajj, M. Hammad","doi":"10.1109/IRI.2013.6642517","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642517","url":null,"abstract":"XML is very attractive for platform independent information exchange, and hence XML streams are becoming very common. In this paper, we discuss and propose an efficient approach to deal with XML streams. Instead of using finite-state automata, we use parse trees in our approach. Moreover, we try to release results to users as soon as possible, through early predicate evaluation. The implemented system accepts for-let-where XQuery queries. After conducting experiments to examine our approach for processing XML streams, we got good results which demonstrate the efficiency, applicability and effectiveness of our approach.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131581996","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}

{"title":"The use of balance-aware subsampling for bioinformatics datasets","authors":"Randall Wald, T. Khoshgoftaar, Alireza Fazelpour","doi":"10.1109/IRI.2013.6642489","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642489","url":null,"abstract":"A major challenge facing data-mining practitioners in the field of bioinformatics is class imbalance, which occurs when instances of one class (called the majority class) vastly outnumber instances of the other (minority) classes. This can result in models with increased bias towards the majority class (minority-class instances predicted as being in the majority class). Data sampling, a process which changes the dataset through removing or adding instances to improve the class balance, can be used to improve the performance of such models on imbalanced data. However, it is not clear what target balance level should be used with data sampling, and what influence class imbalance alone has on classification performance (compared to other issues such as difficulty of learning from the data and dataset size). To resolve this, we propose the Balance-Aware Subsampling technique, which allows researchers to directly compare different balance levels of a dataset while keeping all other factors (such as dataset size and the actual dataset in question) constant. Thus, any changes in performance can be attributed solely to the chosen balance level. We demonstrate this technique using six datasets from the field of bioinformatics, and we also consider three different subsample sizes (that is, the size of the dataset used for building a model) so we can observe the effect of this parameter on classification performance. Our results show that within each level of class imbalance, the average AUC value increases as the subsample size increases. The key exception is the 20:80 (minority:majority) balance level, for which the average AUC value decreases as the subsample size increases from 80 to 120. We also find that within each subsample size, the average AUC value increases as the minority distribution increases, although this does not completely hold for subsample size 40 (in which case, the Näıve Bayes and Random Forest learners show greater performance at the 35:65 balance level than at 50:50), and in general there is not a significant improvement between the 35:65 and 50:50 balance levels. Overall, by using Balance-Aware Subsampling, we are able to directly observe how class imbalance affects performance isolated from all other factors.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134350724","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}

{"title":"Bayesian model for a multicriteria recommender system with support vector regression","authors":"Pannawit Samatthiyadikun, A. Takasu, Saranya Maneeroj","doi":"10.1109/IRI.2013.6642451","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642451","url":null,"abstract":"Recommender systems are becoming very useful for competitive businesses. It is very important for recommender systems to extract user preferences accurately by utilizing logs that record user behavior. Furthermore, user behavior should be analyzed from multiple aspects, storing the results as multicriteria rating scores. If the rating information is sparse, then systems are forced to compensate. One way to treat sparseness is to use a latent model that maps users and items to a small number of groups. To predict rating scores from such a model, we need to aggregate the data appropriately. This paper proposes a method for combining a latent model with a proposed regression technique. We evaluated the proposed method for the Yahoo! Movie data set and show empirically that the proposed combination improves the recommendation accuracy.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133876109","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}

{"title":"Gene selection stability's dependence on dataset difficulty","authors":"D. Dittman, T. Khoshgoftaar, Randall Wald, Amri Napolitano","doi":"10.1109/IRI.2013.6642491","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642491","url":null,"abstract":"Identifying important biomarkers to improve disease diagnosis and treatment is a significant topic of research in bioinformatics. However, bioinformatics datasets frequently have a large number of features per sample or instance. This problem, known as “high dimensionality,” can be alleviated through the use of dimension reducing techniques such as feature (gene) selection which remove unnecessary features. There are many versions of feature selection, with varying biases and predictive abilities. However, predictive power is but one factor to consider when choosing a feature selection technique: one must also consider the technique's stability, that is, its ability to create feature subsets which remain valid in the face of changes to the data. While there has been work in determining the relative stability of different feature selection techniques, this does not always help determine whether a chosen feature selection technique will give stable feature subsets for a specific dataset. Factors such as difficulty of learning (e.g., dataset difficulty) may also influence feature selection stability, making generally-true facts about different techniques not applicable to a given dataset. In this work, we study how dataset difficulty can affect the stability of feature selection techniques, leading to good performance from bad techniques and vice versa. We use a set of twenty-six DNA microarray datasets with varying levels of difficulty of learning, along with four levels of dataset perturbation, six feature selection techniques with various levels of stability, and twelve feature subset sizes. The results show that as the dataset difficulty increases, the stability decreases. However, the relative stability between the techniques remains the same. Additionally, the more difficult the dataset, the more the stability is affected by changes to the data. We also found that unstable rankers are more affected by the transition between Easy and Moderate datasets, whereas the stable techniques are more affected by the change between Moderate and Hard datasets. Lastly, as the feature subset size increases, the stability increases and the difference between the levels of dataset difficulty decreases. Overall, we conclude that difficulty of learning must be taken into account before interpreting stability results.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133995446","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}

{"title":"Comparison of rank-based vs. score-based aggregation for ensemble gene selection","authors":"D. Dittman, T. Khoshgoftaar, Randall Wald, Amri Napolitano","doi":"10.1109/IRI.2013.6642476","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642476","url":null,"abstract":"Gene selection is an essential step in much bioinformatics research in order to handle the thousands or tens of thousands of gene expression levels generated by gene microarrays. It is especially important that this gene selection is robust and will produce consistent results even in the face of changes to the dataset. Ensemble gene selection can help improve robustness, by combining gene rankings from multiple gene selection techniques into a single gene subset. Typically this is performed by performing multiple runs of feature (gene) selection, finding each gene's rank within the different runs, and aggregating these ranks into a final ranked list. However, another option exists: instead of performing the ranking on each list and then aggregating, the raw scores produced by the gene ranking algorithms (which would normally be compared to generate a ranking) are aggregated directly, and these aggregate scores are used to create a final ranking. This potentially results in a different final ranking, since adjacent genes (e.g., those with no genes in between them) which are particularly close to or far from one another will be treated as such. Also, score aggregation can help reduce computation time due to the ranking step only taking place once, rather than separately for each list being aggregated. In this experiment, we use eleven DNA microarray datasets and nine univariate feature selection techniques, along with twelve feature subset sizes, to demonstrate these two approaches on a commonly used aggregation technique: mean aggregation. The results show that for seven of the nine feature selection techniques, we see strong similarity between the two approaches, but the feature subsets are not identical. However, two of the techniques do show high levels of diversity between the two approaches. This allows us to state that further research is required in order to determine the abilities of the two approaches.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"499 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133075578","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}

{"title":"MFWK-Means: Minkowski metric Fuzzy Weighted K-Means for high dimensional data clustering","authors":"L. Svetlova, B. Mirkin, H. Lei","doi":"10.1109/IRI.2013.6642535","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642535","url":null,"abstract":"This paper presents a clustering algorithm, namely MFWK-Means, which is a novel extension of K-Means clustering to the case of fuzzy clusters and weighted features. First, the Weighted K-Means criterion utilizing Minkowski metric is adopted to solve the problem of feature selection for high dimensional data. Then, a further extension to the case of fuzzy clustering is presented to group datasets with natural fuzziness of cluster boundaries. Also, we adopt an intelligent version of K-Means, using Mirkin's method of Anomalous Pattern for initialization. Our new Minkowski metric Fuzzy Weighted K-Means (MFWK-Means) is experimentally validated on both benchmark datasets and synthetic datasets. MFWK-Means is shown to be competitive and more stable against noise in comparison with a variety of versions of K-Means based methods. Moreover, in most situations it reaches the highest clustering accuracy at wider intervals of Minkowski exponent.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129720776","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}

{"title":"Improving the accomplishment of a neural network based agent for draughts that operates in a distributed learning environment","authors":"Lidia Bononi Paiva Tomaz, Rita Maria Silva Julia, Ayres Roberto Araújo Barcelos","doi":"10.1109/IRI.2013.6642481","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642481","url":null,"abstract":"This article presents an extension to the system D-VisionDraughts: a draughts player agent based on a MultiLayer Perceptron Neural Network which operates in a distributed environment, and in a manner which distinguishes it from the current world champion Chinook, it learns without human supervision. The network weights are updated by Temporal Differences Methods using self-play with cloning technique. The best move is chosen by the parallel Alpha-Beta search algorithm called Young Brothers Wait Concept. The representation of the game board states is based on the NET-FEATUREMAP techniques (functions describing features inherent to Draughts game). This paper investigates the improvement obtained by D-VisionDraughts through the insertion of new features that allow a more precise representation of the board states. Further, the authors show to what extent the addition of new processors compensates the increase in training time that would be an obvious consequence of the optimization of the board state representation.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123296436","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}

{"title":"Automatic extraction of effective rule sets for Obstructive Sleep Apnea detection for a real-time mobile monitoring system","authors":"Giovanna Sannino, I. D. Falco, G. Pietro","doi":"10.1109/IRI.2013.6642479","DOIUrl":"https://doi.org/10.1109/IRI.2013.6642479","url":null,"abstract":"Real-time Obstructive Sleep Apnea (OSA) detection and monitoring are important for the society in terms of improvement in citizens' health conditions and of reduction in mortality and healthcare costs. This paper proposes an easy, cheap, and portable approach for monitoring patients with OSA. It is based on singlechannel ECG data, and on the automatic offline extraction, from a database containing ECG information about the monitored patient, of explicit knowledge under the form of a set of IF...THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. This set of rules can be exploited in our realtime mobile monitoring system: ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time, HRV-related parameters are computed from it, and, if their values activate some of the rules describing occurrence of OSA, an alarm is automatically produced. The approach has been tested on a well-known literature database of OSA patients. Rules are obtained which are specific for each patient. Numerical results have shown the effectiveness of the approach, and the achieved sets of rules evidence its user-friendliness. Furthermore, the method has been compared against other well-known classifiers.","PeriodicalId":418492,"journal":{"name":"2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132032118","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}