2014 13th International Conference on Machine Learning and Applications最新文献

{"title":"Performance Comparison of Major Classical Face Recognition Techniques","authors":"F. Bhat, M. A. Wani","doi":"10.1109/ICMLA.2014.91","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.91","url":null,"abstract":"The goal of this paper is to present a critical comparison of existing classical techniques on recognition of human faces. This paper describes the four major classical face recognition techniques i.e., i) Principal Component Analysis (PCA), ii) Linear Discriminant Analysis (LDA), iii) Discrete Cosine Transform (DCT), and iv) Independent Component Analysis (ICA). Strong and weak features of these techniques are discussed. The paper then provides performance comparison and a generalized discussion of the training requirements for these face recognition techniques. Extensive experimental results with three publicly available databases (ORL, Yale, FERET databases) are provided. Performance comparison of recognizing face images taken under varying facial expressions, varying lighting condition and varying poses are discussed.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130707322","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":"OUPS: A Combined Approach Using SMOTE and Propensity Score Matching","authors":"William A. Rivera, Amit Goel, J. Kincaid","doi":"10.1109/ICMLA.2014.106","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.106","url":null,"abstract":"Building accurate classifiers is difficult when using data that is skewed or imbalanced which is typical of real world data sets. Two popular approaches that have been applied for improving classification accuracy and statistical comparisons of imbalanced data sets are: synthetic minority over-sampling technique (SMOTE) and propensity score matching (PSM). A novel sampling approach is introduced referred to as over-sampling using propensity scores (OUPS) that blends the two and is simple and easy to perform resulting in improvement in accuracy and sensitivity over both SMOTE and PSM. The performance of our proposed approach is assessed using a simulation experiment and several performance metrics are shown where this approach fares and falls in comparison to the others.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130534857","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":"Learning Score Systems for Patient Mortality Prediction in Intensive Care Units via Orthogonal Matching Pursuit","authors":"Aadirupa Saha, Chandrahas, H. Narasimhan, S. Sampath, S. Agarwal","doi":"10.1109/ICMLA.2014.20","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.20","url":null,"abstract":"The problem of predicting outcome of patients in intensive care units (ICUs) is of great importance in critical care medicine, and has wide implications for quality control in ICUs. A dominant approach to this problem has been to use an ICU score system such as, for example, the Acute Physiology and Chronic Health Evaluation (APACHE) system, and the Simplified Acute Physiology Score (SAPS) system, to compute a certain severity score for a patient from a set of clinical observations, and apply a logistic regression model on this score to obtain an estimate of the probability of mortality for the patient, owing to their simplicity, these methods are widely used by clinicians. However, existing ICU score systems are built from a fixed set of patient data, and often perform poorly when applied to a patient population with different characteristics, also, with changes in patient characteristics, a score system built from a given patient data set becomes suboptimal over time. Moreover, most of these score systems are built using semi-automated procedures that require some amount of manual intervention, making it difficult to adapt them to a new patient population. Thus there is a huge need for adaptive methods that can automatically learn predictive models from a given set of patient data, tailored to perform well on similar patient populations. Indeed, there has been much work in recent years on applying various machine learning methods to this problem, however these methods learn different representations from the score systems preferred by clinicians. In this work, we develop a machine learning method based on orthogonal matching pursuit that automatically learns a score system type model, which enjoys the benefits of both worlds: like other machine learning methods, it is adaptive, like standard score systems, it uses a representation that is easy for clinicians to understand. Experiments on real-world patient data sets show that our method outperforms standard ICU score systems, and performs at least as well as other machine learning methods that employ more complex representations. As an added advantage of using the OMP approach, one can use a group-sparse variant of OMP which allows learning models with similar performance using a smaller number of clinical observations, we include experiments with this as well.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132850303","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":"Learning Good Features to Track","authors":"Raed Almomani, Ming Dong, Zhou Liu","doi":"10.1109/ICMLA.2014.66","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.66","url":null,"abstract":"Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are trained offline based on unlabeled data. In tracking, the collected positive and negative samples from the previously tracked frames are used to learn good features for a specific target. OCNN is also augmented with a classifier to provide a decision. We build a tracking system by combining OCNN and a color-based multi-appearance model. Our experimental results on publicly available video datasets show that the tracking system has superior performance when compared with several state of-the-art trackers.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132798263","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":"Learner Engagement Measurement and Classification in 1:1 Learning","authors":"Sinem Aslan, Z. Cataltepe, Itai Diner, O. Dundar, Asli Arslan Esme, Ron Ferens, Gila Kamhi, Ece Oktay, Canan Soysal, M. Yener","doi":"10.1109/ICMLA.2014.111","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.111","url":null,"abstract":"We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131920187","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":"A New Algorithm for Adaptive Online Selection of Auxiliary Objectives","authors":"Arina Buzdalova, M. Buzdalov","doi":"10.1109/ICMLA.2014.100","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.100","url":null,"abstract":"Consider optimization problems, where a target objective should be optimized. Some auxiliary objectives can be used to obtain the optimum of the target objective in less number of objective evaluations. We call such auxiliary objective a supporting one. Usually there is no prior knowledge about properties of auxiliary objectives, some objectives can be obstructive as well. What is more, an auxiliary objective can be both supporting and obstructive at different stages of the target objective optimization. Thus, an adaptive online method of objective selection is needed. Earlier, we proposed a method for doing that, which is based on reinforcement learning. In this paper, a new algorithm for adaptive online selection of optimization objectives is proposed. The algorithm meets the interface of a reinforcement learning agent, so it can be fit into the previously proposed framework. The new algorithm is applied for solving some benchmark problems with single-objective evolutionary algorithms. Specifically, Leading Ones with OneMax auxiliary objective is considered, as well as the MH-IFF problem. Experimental results are presented. The proposed algorithm outperforms Q-learning and random objective selection on the considered problems.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132125848","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":"Improved kNN Rule for Small Training Sets","authors":"Sunsern Cheamanunkul, Y. Freund","doi":"10.1109/ICMLA.2014.37","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.37","url":null,"abstract":"The traditional k-NN classification rule predicts a label based on the most common label of the k nearest neighbors (the plurality rule). It is known that the plurality rule is optimal when the number of examples tends to infinity. In this paper we show that the plurality rule is sub-optimal when the number of labels is large and the number of examples is small. We propose a simple k-NN rule that takes into account the labels of all of the neighbors, rather than just the most common label. We present a number of experiments on both synthetic datasets and real-world datasets, including MNIST and SVHN. We show that our new rule can achieve lower error rates compared to the majority rule in many cases.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"57 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134555263","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":"An Intelligent Tutoring System for Argument-Making in Higher Education: A Pilot Study","authors":"C. Chuan, Daniel L. Dinsmore, J. Schmuller, Tyler Morris","doi":"10.1109/ICMLA.2014.112","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.112","url":null,"abstract":"This paper presents a pilot study on an intelligent tutoring system for domain-independent argument making. Students' responses to an open-ended question were collected as the instances for supervised text classification based on the grade given by the instructor using structured outcome of the learning observation taxonomy. The responses were processed using Cohmetrix as well as n-gram models to generate attributes for the classification task. The best result of 81.74% in classification correct rate was obtained when all grade classes were used.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114191529","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":"Assessment of Different Image Clutter Metrics Using Multivariate Analyses and Neurofuzzy System","authors":"D. Nam, Harpreet Singh","doi":"10.1109/ICMLA.2014.92","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.92","url":null,"abstract":"Image processing is the most frequently used technique in computer vision like target detection of monitored target images to recognize background clutter and observed target images. To evaluate the performance of various image processing algorithms, image clutter metrics are very important and useful factors for the better visual conception such as increasing the probability of detection, decreasing the false alarm rate, or a relatively shorter searching time. In this paper, different image clutter metrics such as probability of detection, false alarm rate, and search time, are assessed by the statistical analysis techniques and neurofuzzy systems through applying other statistical image clutter metrics in order to improve the machine visual conception with resolving the machine cognitive constraints for the computer vision.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771110","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":"An Intelligent Technique for Detecting Malicious Users on Mobile Stores","authors":"Ramazan Terzi, U. Yavanoglu, Duygu Sinanc, Dogac Oguz, Semra Cakir","doi":"10.1109/ICMLA.2014.82","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.82","url":null,"abstract":"In this study, malicious users who cause to resource exhausting are tried to detect in a telecommunication company network. Non-Legitimate users could cause lack of information availability and need countermeasures to prevent threat or limit permissions on the system. For this purpose, ANN based intelligent system is proposed and compared to SVM which is well known classification technique. According to results, proposed technique has achieved approximately 70% general success rate, 33% false positive rate and 27% false negative rate in controlled environment. Also ANN has high ability to work compare to SVM for our dataset. As a result proposed technique and developed application shows sufficient and acceptable defense mechanism in huge company networks. We discussed about this is initial study and ongoing research which is compared to the current literature. By the way, this study also shows that non security information such as users mobile experiences could be potential usage to prevent resource exhausting also known as DoS related attacks.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122132322","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}