Contributions for the improvement of specific class mapping

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe analysis of remotely sensed imagery has become a fundamental task for many environmental centred activities, not just scientific but also management related. In particular, the use of land cover maps depicting a particular study site is an integral part of many research projects, as they are not just a fundamental variable in environmental models but also base information supporting policy decisions. Land cover mapping assisted by supervised classification is today a staple tool of any analyst processing remotely sensed data, insomuch as these techniques allow users to map entire sites of interest in a omprehensive way. Many remote sensing projects are usually interested in a small number of land cover classes present in a study area and not in all classes that make-up the landscape. When focus is on a particular sub-set of classes of interest, conventional supervised classification may be sub-optimal for the discrimination of these specific target classes. The process of producing a non-exhaustive map, that is depicting only the classes of interest for the user, is called specific class mapping. This is the topic of this dissertation. Here, specific class mapping is examined to understand its origins, developments, adoption and current limitations. The main research goal is then to contribute for the understanding and improvement of this topic, while presenting its main constrains in a clear way and proposing enhanced methods at the reach of the non-expert user. In detail, this study starts by analysing the definition of specific class mapping and why the conventional multi-class supervised classification process may yield sub-optimal outcomes. Attention then is turn to the previous works that have tackled this problem. From here a synthesis is made, categorising and characterising previous methodologies. Its then learnt that the methodologies tackling specific class mapping fall under two broad categories, the binarisation approaches and the singe-class approaches, and that both types are not without problems. This is the starting point of the development component of this dissertation that branches out in three research lines. First, cost-sensitive learning is utilised to improve specific class mapping. In previous studies it was shown that it may be susceptible to data imbalance problems present in the training data set, since the classes of interest are often a small part of the training set. As a result the classification may be biased towards the largest classes and, thus, be sub-optimal for the discrimination of the classes of interest. Here cost-sensitive learning is used to balance the training data set to minimise the effects of data imbalance. In this approach errors committed in the minority class are treated as being costlier than errors committed in the majority class. Cost-sensitive approaches are typically implemented by weighting training data points accordingly to their importance to the analysis. By shifting the weight of the data set from the majority class to the minority class, the user is capable to inform the learning process that training data points in the minority class are as critical as the points in the majority class. The results of this study indicate that this simple approach is capable to improve the process of specific class mapping by increasing the accuracy to which the classes of interest are discriminated. Second, the combined use single-class classifiers for specific class mapping is explored. Supervised algorithms for single-class classification are particularly attractive due to its reduced training requirements. Unlike other methods where all classes present in the study site regardless of its relevance for the particular objective to the users, single-class classifiers rely exclusively on the training of the class of interest. However, these methods can only solve specific classification problems with one class of interest. If more classes are important, those methods cannot be directly utilised. Here is proposed three combining methodologies to combine single-class classifiers to map subsets of land cover classes. The results indicate that an intelligent combination of single-class classifiers can be used to achieve accurate results, statistically noninferior to the standard multi-class classification, without the need of an exhaustive training set, saving resources that can be allocated to other steps of the data analysis process. Third, the combined use of cost-sensitive and semi-supervised learning to improve specific class mapping is explored. A limitation of the specific class binary approaches is that they still require training data from secondary classes, and that may be costly. On the other hand, a limitation of the specific class single-class approaches is that, while requiring only training data from the specific classes of interest, this method tend to overestimate the extension of the classes of interest. This is because the classifier is trained without information about the negative part of the classification space. A way to overcome this is with semi-supervised learning, where the data points for the negative class are randomly sampled from the classification space. However that may include false negatives. To overcome this difficult, cost-sensitive learning is utilised to mitigate the effect of these potentially misclassified data points. Cost weights were here defined using an exponential model that assign more weight to the negative data points that are more likely to be correctly labelled and less to the points that are more likely to be mislabelled. The results show that accuracy achieved with the proposed method is statistically non-inferior to that achieved with standard binary classification requiring however much less training effort

Development of a Cost-Efficient Multi-Target Classification System Based on FMCW Radar for Security Gate Monitoring

Radar systems have a long history. Like many other great inventions, the origin of radar systems lies in warfare. Only in the last decade, radar systems have found widespread civil use in industrial measurement scenarios and automotive safety applications. Due to their resilience against harsh environments, they are used instead of or in addition to optical or ultrasonic systems. Radar sensors hold excellent capabilities to estimate distance and motion accurately, penetrate non-metallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Electromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna, and circuitry design, and lower atmospheric absorption than higher frequency-based systems. This thesis studies non-cooperative automatic radar multi-target detection and classification. A prototype of a radar system with a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets passing through a road gate is presented. It allows identifying different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which takes care of detection and classification. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by the radar. The developed method is based on an ad-hoc processing chain to accomplish the automatic target recognition task, which consists of blocks performing clutter and leakage removal with a frame subtraction technique, clustering with a DBSCAN approach, tracking algorithm based on the \u3b1-\u3b2 filter to follow the targets during traversal, features extraction, and finally classification of targets with a classification scheme based on support vector machines. The approach is validated in real experimental scenarios, showing its capabilities incorrectly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities

Feature selection and hierarchical classifier design with applications to human motion recognition

The performance of a classifier is affected by a number of factors including classifier type, the input features and the desired output. This thesis examines the impact of feature selection and classification problem division on classification accuracy and complexity. Proper feature selection can reduce classifier size and improve classifier performance by minimizing the impact of noisy, redundant and correlated features. Noisy features can cause false association between the features and the classifier output. Redundant and correlated features increase classifier complexity without adding additional information. Output selection or classification problem division describes the division of a large classification problem into a set of smaller problems. Problem division can improve accuracy by allocating more resources to more difficult class divisions and enabling the use of more specific feature sets for each sub-problem. The first part of this thesis presents two methods for creating feature-selected hierarchical classifiers. The feature-selected hierarchical classification method jointly optimizes the features and classification tree-design using genetic algorithms. The multi-modal binary tree (MBT) method performs the class division and feature selection sequentially and tolerates misclassifications in the higher nodes of the tree. This yields a piecewise separation for classes that cannot be fully separated with a single classifier. Experiments show that the accuracy of MBT is comparable to other multi-class extensions, but with lower test time. Furthermore, the accuracy of MBT is significantly higher on multi-modal data sets. The second part of this thesis focuses on input feature selection measures. A number of filter-based feature subset evaluation measures are evaluated with the goal of assessing their performance with respect to specific classifiers. Although there are many feature selection measures proposed in literature, it is unclear which feature selection measures are appropriate for use with different classifiers. Sixteen common filter-based measures are tested on 20 real and 20 artificial data sets, which are designed to probe for specific feature selection challenges. The strengths and weaknesses of each measure are discussed with respect to the specific feature selection challenges in the artificial data sets, correlation with classifier accuracy and their ability to identify known informative features. The results indicate that the best filter measure is classifier-specific. K-nearest neighbours classifiers work well with subset-based RELIEF, correlation feature selection or conditional mutual information maximization, whereas Fisher's interclass separability criterion and conditional mutual information maximization work better for support vector machines. Based on the results of the feature selection experiments, two new filter-based measures are proposed based on conditional mutual information maximization, which performs well but cannot identify dependent features in a set and does not include a check for correlated features. Both new measures explicitly check for dependent features and the second measure also includes a term to discount correlated features. Both measures correctly identify known informative features in the artificial data sets and correlate well with classifier accuracy. The final part of this thesis examines the use of feature selection for time-series data by using feature selection to determine important individual time windows or key frames in the series. Time-series feature selection is used with the MBT algorithm to create classification trees for time-series data. The feature selected MBT algorithm is tested on two human motion recognition tasks: full-body human motion recognition from joint angle data and hand gesture recognition from electromyography data. Results indicate that the feature selected MBT is able to achieve high classification accuracy on the time-series data while maintaining a short test time

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered \de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to di erent type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several di erent domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also signi cantly contributed to new supervised learning paradigms, including multilabel classi cation, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of di erent software packages | from open source to commercial. In this paper, marking the fteen year anniversary of SMOTE, we re ect on the SMOTE journey, discuss the current state of a airs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.This work have been partially supported by the Spanish Ministry of Science and Technology under projects TIN2014-57251-P, TIN2015-68454-R and TIN2017-89517-P; the Project 887 BigDaP-TOOLS - Ayudas Fundaci on BBVA a Equipos de Investigaci on Cient ca 2016; and the National Science Foundation (NSF) Grant IIS-1447795

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Learning from Multi-Class Imbalanced Big Data with Apache Spark

With data becoming a new form of currency, its analysis has become a top priority in both academia and industry, furthering advancements in high-performance computing and machine learning. However, these large, real-world datasets come with additional complications such as noise and class overlap. Problems are magnified when with multi-class data is presented, especially since many of the popular algorithms were originally designed for binary data. Another challenge arises when the number of examples are not evenly distributed across all classes in a dataset. This often causes classifiers to favor the majority class over the minority classes, leading to undesirable results as learning from the rare cases may be the primary goal. Many of the classic machine learning algorithms were not designed for multi-class, imbalanced data or parallelism, and so their effectiveness has been hindered. This dissertation addresses some of these challenges with in-depth experimentation using novel implementations of machine learning algorithms using Apache Spark, a distributed computing framework based on the MapReduce model designed to handle very large datasets. Experimentation showed that many of the traditional classifier algorithms do not translate well to a distributed computing environment, indicating the need for a new generation of algorithms targeting modern high-performance computing. A collection of popular oversampling methods, originally designed for small binary class datasets, have been implemented using Apache Spark for the first time to improve parallelism and add multi-class support. An extensive study on how instance level difficulty affects the learning from large datasets was also performed

Topical subcategory structure in text classification

Data sets with rich topical structure are common in many real world text classification tasks. A single data set often contains a wide variety of topics and, in a typical task, documents belonging to each class are dispersed across many of the topics. Often, a complex relationship exists between the topic a document discusses and the class label: positive or negative sentiment is expressed in documents from many different topics, but knowing the topic does not necessarily help in determining the sentiment label. We know from tasks such as Domain Adaptation that sentiment is expressed in different ways under different topics. Topical context can in some cases even reverse the sentiment polarity of words: to be sharp is a good quality for knives but bad for singers. This property can be found in many different document classification tasks. Standard document classification algorithms do not account for or take advantage of topical diversity; instead, classifiers are usually trained with the tacit assumption that topical diversity does not play a role. This thesis is focused on the interplay between the topical structure of corpora, how the target labels in a classification task distribute over the topics and how the topical structure can be utilised in building ensemble models for text classification. We show empirically that a dataset with rich topical structure can be problematic for single classifiers, and we develop two novel ensemble models to address the issues. We focus on two document classification tasks: document level sentiment analysis of product reviews and hierarchical categorisation of news text. For each task we develop a novel ensemble method that utilises topic models to address the shortcomings of traditional text classification algorithms. Our contribution is in showing empirically that the class association of document features is topic dependent. We show that using the topical context of documents for building ensembles is beneficial for some tasks, and present two new ensemble models for document classification. We also provide a fresh viewpoint for reasoning about the relationship of class labels, topical categories and document features

Optimising WLANs Power Saving: Context-Aware Listen Interval

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously. We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applications’ network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applications’ network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction. Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applications’ network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALI’s power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier

Improving human movement sensing with micro models and domain knowledge

Human sensing is concerned with techniques for inferring information about humans from various sensing modalities. Examples of human sensing applications include human activity (or action) recognition, emotion recognition, tracking and localisation, identification, presence and motion detection, occupancy estimation, gesture recognition, and breath rate estimation. The first question addressed in this thesis is whether micro or macro models are a better design choice for human sensing systems. Micro models are models exclusively trained with data from a single entity, such as a Wi-Fi link, user, or other identifiable data-generating component. We consider micro and macro models in two human sensing applications, viz. Human Activity Recognition (HAR) from wearable inertial sensor data and device-free human presence detection from Wi-Fi signal data. The HAR literature is dominated by person-independent macro models. The few empirical studies that consider both micro and macro models evaluate them with either only one data-set or only one HAR algorithm, and report contradictory results. The device-free sensing literature is dominated by link-specific micro models, and the few papers that do use macro models do not evaluate their micro counterparts. Given the little and contradictory evidence, it remains an open question whether micro or macro models are a better design choice. We evaluate person-specific micro and person-independent macro models across seven HAR benchmark data-sets and four learning algorithms. We show that person-specific models (PSMs) significantly outperform the corresponding person-independent model (PIM) when evaluated with known users. To apply PSMs to data from new users, we propose ensembles of PSMs, which are improved by weighting their constituent PSMs according to their performance on other training users. We propose link-specific micro models to detect human presence from ambient Wi-Fi signal data. We select a link-specific model from the available training links, and show that this approach outperforms multi-link macro models. The second question addressed in this thesis is whether human sensing methods can be improved with domain knowledge. Specifically, we propose expert hierarchies (EHs) as an intuitive way to encode domain knowledge and simplify multi-class HAR, without negatively affecting predictive performance. The advantages of EHs are that they have lower time complexity than domain-agnostic methods and that their constituent classifiers are statistically independent. This property enables targeted tuning, and modular and iterative development of increasingly fine-grained HAR. Although this has inspired several uses of domain-specific hierarchical classification for HAR applications, these have been ad-hoc and without comparison to standard domain-agnostic methods. Therefore, it remains unclear whether they carry a penalty on predictive performance. We design five EHs and compare them to the best-known domain-agnostic methods. Our results show that EHs indeed can compete with more popular multi-class classification methods, both on the original multi-class problem and on the EHs' topmost levels