Comparing Multi-objective and Threshold-moving ROC Curve Generation for a Prototype-based Classifier

Proceedings of: GECCO 2013: 15th International Conference on Genetic and Evolutionary Computation Conference (Amsterdam, The Netherlands, July 06-10, 2013): a recombination of the 22nd International Conference on Genetic Algorithms (ICGA) and the 18th Annual Genetic Programming Conference (GP), Amsterdam, The Netherlands, July 06-10, 2013Receiver Operating Characteristics (ROC) curves represent the performance of a classifier for all possible operating con-ditions, i.e., for all preferences regarding the tradeoff be-tween false positives and false negatives. The generation of a ROC curve generally involves the training of a single classifier for a given set of operating conditions, with the subsequent use of threshold-moving to obtain a complete ROC curve. Recent work has shown that the generation of ROC curves may also be formulated as a multi-objective optimization problem in ROC space: the goals to be min-imized are the false positive and false negative rates. This technique also produces a single ROC curve, but the curve may derive from operating points for a number of different classifiers. This paper aims to provide an empirical compar-ison of the performance of both of the above approaches, for the specific case of prototype-based classifiers. Results on synthetic and real domains shows a performance advantage for the multi-objective approach.GECCO 2013 Presentation slidesThis work has been funded by the Spanish Ministry of Science under contract TIN2011-28336 (MOVES project)En prens

Distributed Online Machine Learning for Mobile Care Systems

Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis

Identification, indexing, and retrieval of cardio-pulmonary resuscitation (CPR) video scenes of simulated medical crisis.

Medical simulations, where uncommon clinical situations can be replicated, have proved to provide a more comprehensive training. Simulations involve the use of patient simulators, which are lifelike mannequins. After each session, the physician must manually review and annotate the recordings and then debrief the trainees. This process can be tedious and retrieval of specific video segments should be automated. In this dissertation, we propose a machine learning based approach to detect and classify scenes that involve rhythmic activities such as Cardio-Pulmonary Resuscitation (CPR) from training video sessions simulating medical crises. This applications requires different preprocessing techniques from other video applications. In particular, most processing steps require the integration of multiple features such as motion, color and spatial and temporal constrains. The first step of our approach consists of segmenting the video into shots. This is achieved by extracting color and motion information from each frame and identifying locations where consecutive frames have different features. We propose two different methods to identify shot boundaries. The first one is based on simple thresholding while the second one uses unsupervised learning techniques. The second step of our approach consists of selecting one key frame from each shot and segmenting it into homogeneous regions. Then few regions of interest are identified for further processing. These regions are selected based on the type of motion of their pixels and their likelihood to be skin-like regions. The regions of interest are tracked and a sequence of observations that encode their motion throughout the shot is extracted. The next step of our approach uses an HMM classiffier to discriminate between regions that involve CPR actions and other regions. We experiment with both continuous and discrete HMM. Finally, to improve the accuracy of our system, we also detect faces in each key frame, track them throughout the shot, and fuse their HMM confidence with the region\u27s confidence. To allow the user to view and analyze the video training session much more efficiently, we have also developed a graphical user interface (GUI) for CPR video scene retrieval and analysis with several desirable features. To validate our proposed approach to detect CPR scenes, we use one video simulation session recorded by the SPARC group to train the HMM classifiers and learn the system\u27s parameters. Then, we analyze the proposed system on other video recordings. We show that our approach can identify most CPR scenes with few false alarms

Multivariate Stochastic Approximation to Tune Neural Network Hyperparameters for Criticial Infrastructure Communication Device Identification

The e-government includes Wireless Personal Area Network (WPAN) enabled internet-to-government pathways. Of interest herein is Z-Wave, an insecure, low-power/cost WPAN technology increasingly used in critical infrastructure. Radio Frequency (RF) Fingerprinting can augment WPAN security by a biometric-like process that computes statistical features from signal responses to 1) develop an authorized device library, 2) develop classifier models and 3) vet claimed identities. For classification, the neural network-based Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is employed. GRLVQI has shown high fidelity in classifying Z-Wave RF Fingerprints; however, GRLVQI has multiple hyperparameters. Prior work optimized GRLVQI via a full factorial experimental design. Herein, optimizing GRLVQI via stochastic approximation, which operates by iterative searching for optimality, is of interest to provide an unconstrained optimization approach to avoid limitations found in full factorial experimental designs. The results provide an improvement in GRLVQI operation and accuracy. The methodology is further generalizable to other problems and algorithms

Multi-Cue Pedestrian Recognition

This thesis addresses the problem of detecting complex, deformable objects in an arbitrary, cluttered environment in sequences of video images. Often, no single best technique exists for such a challenging problem, as different approaches possess different characteristics with regard to detection accuracy, processing speed, or the kind of errors made. Therefore, multi-cue approaches are pursued in this thesis. By combining multiple detection methods, each utilizing a different aspect of the video images, we seek to gain detection accuracy, robustness, and computational efficiency. The first part of this thesis deals with texture classification. In a comparative study, various combinations of feature extraction and classification methods, some of which novel, are examined with respect to classification performance and processing speed, and the relation to the training sample size is analyzed. The integration of shape matching and texture classification is investigated. A pose-specific mixture-of-experts architecture is proposed, where shape matching yields a probabilistic assignment of a texture pattern to a set of distinct pose clusters, each handled by a specialized texture classifier, the local expert. The reduced appearance variability that each local expert needs to cope with leads to improved classification performance. A slight further performance gain could be achieved by shape normalization. The second multi-cue approach deals with cascade systems that employ a sequence of fast-to-complex system modules in order to gain computational efficiency. Three optimization techniques are examined that adjust system parameters so as to optimize the three performance measures detection rate, false positive rate, and processing cost. A combined application of two techniques, a novel fast sequential optimization scheme based on ROC (receiver operating characteristics) frontier following, followed by an iterative gradient descent optimization method, is found to work best. The third method investigated is a Bayesian combination of multiple visual cues. An integrated object detection and tracking framework based on particle filtering is presented. A novel object representation combines mixture models of shape and texture, the former based on a generative point distribution model, the latter on discriminative texture classifiers. The associated observation density function integrates the three visual cues shape, texture, and depth. All methods are extensively evaluated on the problem of detecting pedestrians in urban environment from within a moving vehicle. Large data sets consisting of tens of thousands of video images have been recorded in order to obtain statistically meaningful results

On the class overlap problem in imbalanced data classification.

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy. First, we present a thorough experimental comparison of class overlap and class imbalance. Unlike previous work, our experiment was carried out on the full scale of class overlap and an extreme range of class imbalance degrees. Second, we provide an in-depth critical technical review of existing approaches to handle imbalanced datasets. Existing solutions from selective literature are critically reviewed and categorised as class distribution-based and class overlap-based methods. Emerging techniques and the latest development in this area are also discussed in detail. Experimental results in this paper are consistent with existing literature and show clearly that the performance of the learning algorithm deteriorates across varying degrees of class overlap whereas class imbalance does not always have an effect. The review emphasises the need for further research towards handling class overlap in imbalanced datasets to effectively improve learning algorithms’ performance

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

An attention model and its application in man-made scene interpretation

The ultimate aim of research into computer vision is designing a system which interprets its surrounding environment in a similar way the human can do effortlessly. However, the state of technology is far from achieving such a goal. In this thesis different components of a computer vision system that are designed for the task of interpreting man-made scenes, in particular images of buildings, are described. The flow of information in the proposed system is bottom-up i.e., the image is first segmented into its meaningful components and subsequently the regions are labelled using a contextual classifier. Starting from simple observations concerning the human vision system and the gestalt laws of human perception, like the law of “good (simple) shape” and “perceptual grouping”, a blob detector is developed, that identifies components in a 2D image. These components are convex regions of interest, with interest being defined as significant gradient magnitude content. An eye tracking experiment is conducted, which shows that the regions identified by the blob detector, correlate significantly with the regions which drive the attention of viewers. Having identified these blobs, it is postulated that a blob represents an object, linguistically identified with its own semantic name. In other words, a blob may contain a window a door or a chimney in a building. These regions are used to identify and segment higher order structures in a building, like facade, window array and also environmental regions like sky and ground. Because of inconsistency in the unary features of buildings, a contextual learning algorithm is used to classify the segmented regions. A model which learns spatial and topological relationships between different objects from a set of hand-labelled data, is used. This model utilises this information in a MRF to achieve consistent labellings of new scenes

Supervised Classification and Mathematical Optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data

Supervised classification and mathematical optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.Ministerio de Ciencia e InnovaciónJunta de Andalucí