Popular Ensemble Methods: An Empirical Study

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees

Developing improved algorithms for detection and analysis of skin cancer

University of Technology Sydney. Faculty of Engineering and Information Technology.Malignant melanoma is one of the deadliest forms of skin cancer and number of cases showed rapid increase in Europe, America, and Australia over the last few decades. Australia has one of the highest rates of skin cancer in the world, at nearly four times the rates in Canada, the US and the UK. Cancer treatment costs constitute more 7.2% of health system costs. However, a recovery rate of around 95% can be achieved if melanoma is detected at an early stage. Early diagnosis is obviously dependent upon accurate assessment by a medical practitioner. The variations of diagnosis are sufficiency large and there is a lack of detail of the test methods. This thesis investigates the methods for automated analysis of skin images to develop improved algorithms and to extend the functionality of the existing methods used in various stages of the automated diagnostic system. This in the long run can provide an alternative basis for researchers to experiment new and existing methodologies for skin cancer detection and diagnosis to help the medical practitioners. The objective is to have a detailed investigation for the requirements of automated skin cancer diagnostic systems, improve and develop relevant segmentation, feature selection and classification methods to deal with complex structures present in both dermoscopic/digital images and histopathological images. During the course of this thesis, several algorithms were developed. These algorithms were used in skin cancer diagnosis studies and some of them can also be applied in wider machine learning areas. The most important contributions of this thesis can be summarized as below: - Developing new segmentation algorithms designed specifically for skin cancer images including digital images of lesions and histopathalogical images with attention to their respective properties. The proposed algorithm uses a two-stage approach. Initially coarse segmentation of lesion area is done based on histogram analysis based orientation sensitive fuzzy C Mean clustering algorithm. The result of stage 1 is used for the initialization of a level set based algorithm developed for detecting finer differentiating details. The proposed algorithms achieved true detection rate of around 93% for external skin lesion images and around 88% for histopathological images. - Developing adaptive differential evolution based feature selection and parameter optimization algorithm. The proposed method is aimed to come up with an efficient approach to provide good accuracy for the skin cancer detection, while taking care of number of features and parameter tuning of feature selection and classification algorithm, as they all play important role in the overall analysis phase. The proposed method was also tested on 10 standard datasets for different kind of cancers and results shows improved performance for all the datasets compared to various state-of the art methods. - Proposing a parallelized knowledge based learning model which can make better use of the differentiating features along with increasing the generalization capability of the classification phase using advised support vector machine. Two classification algorithms were also developed for skin cancer data analysis, which can make use of both labelled and unlabelled data for training. First one is based on semi advised support vector machine. While the second one based on Deep Learning approach. The method of integrating the results of these two methods is also proposed. The experimental analysis showed very promising results for the appropriate diagnosis of melanoma. The classification accuracy achieved with the help of proposed algorithms was around 95% for external skin lesion classification and around 92 % for histopathalogical image analysis. Skin cancer dataset used in this thesis is obtained mainly from Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital. While for comparative analysis and benchmarking of the few algorithms some standard online cancer datasets were also used. Obtained result shows a good performance in segmentation and classification and can form the basis of more advanced computer aided diagnostic systems. While in future, the developed algorithms can also be extended for other kind of image analysis applications

Subject Index

Subject Index listed A-Z (8 pages) A a parameter: 299-300, 303, 305-306, 314 absolute standard procedure: 329 absolute standards: 222-223 accuracy of the inference: 150 Adaptive Mastery Testing (AMT): 298,305 advisory committee: 100-101 , 103-105,111- 112 AERA/APA/NCME Standards: 33,37-43, 53, 55 , 66, 68, 72, 84, 94, 11 4, 118, 122, 137-138,168, 170- 171,179,185, 236, 248, 254-255, 26 1, 281 all -in-one requirement: 155 alternate-choice: 120 alternate-choice multiple choice: 324 Americans with Disabilities Act of 1990 (ADA): 22-23, 43, 54-55, 60-6 1, 63, 65, 68-70, 297 amplified objective: 129,147 amplified objective method: 129 analysis of variance (ANaYA): 153 anchor items: 137 Angoff: 223-225,237, 246 Angoff method: 162, 3 17 archive: 196 assessment of a product: 151 authentic assessment: 327-329,339,341 automated item selection: 197 automated item writing: 195 ... W weighted scoring: 152 Wright, Mead, and Draba method: 2 1

Hierarchical Image Classification Threshold On Mangrove

Mangrove area is an important coastal ecosystem in a tropical region. Managing mangrove is challenging and complex. In order to balance between protection the ecosystem and providing the natural resources that benefits to human being. In addition traditional classification and identification of mangrove tree species require an expert inspector working manually. A demand for accurate and automatic of mangrove species estimation has arose especially for ecological, environmental and economical values. Economically, the knowledge of tree species information is important. In order to meet the mangrove forest planning requirements, the satellite remote sensing with high spatial resolution has been specifically designed for tree species classification to improve accuracy and able to locate preferred tree species. However the main issue in remote sensing is image classification that required to determine an appropriate threshold between species in producing accurate classification map. An image classification on satellite imagery is a complex process and requires consideration of accurate classification system. A pixel in the satellite image may possibly cover more than one object on the ground. A threshold has to be set to classify an overlap of two or more associated spectral properties. Therefore the aim of this study is to determine the optimal threshold value for object classes to ensure the misclassification of image pixels kept as low as possible by analyzing the classification of satellite images at different hierarchical level. Then the optimal threshold will be proposed on satellite image classification for mangrove species with the help of expert inspector from the ground. An evaluation on the accuracy of the proposed threshold value in identifying mangrove shall be made. A hierarchical threshold is expected to significant improvement result on an image classification final map for mangrove species

Risk Adjustment In Neurocritical care (RAIN)--prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study.

OBJECTIVES: To validate risk prediction models for acute traumatic brain injury (TBI) and to use the best model to evaluate the optimum location and comparative costs of neurocritical care in the NHS. DESIGN: Cohort study. SETTING: Sixty-seven adult critical care units. PARTICIPANTS: Adult patients admitted to critical care following actual/suspected TBI with a Glasgow Coma Scale (GCS) score of < 15. INTERVENTIONS: Critical care delivered in a dedicated neurocritical care unit, a combined neuro/general critical care unit within a neuroscience centre or a general critical care unit outside a neuroscience centre. MAIN OUTCOME MEASURES: Mortality, Glasgow Outcome Scale - Extended (GOSE) questionnaire and European Quality of Life-5 Dimensions, 3-level version (EQ-5D-3L) questionnaire at 6 months following TBI. RESULTS: The final Risk Adjustment In Neurocritical care (RAIN) study data set contained 3626 admissions. After exclusions, 3210 patients with acute TBI were included. Overall follow-up rate at 6 months was 81%. Of 3210 patients, 101 (3.1%) had no GCS score recorded and 134 (4.2%) had a last pre-sedation GCS score of 15, resulting in 2975 patients for analysis. The most common causes of TBI were road traffic accidents (RTAs) (33%), falls (47%) and assault (12%). Patients were predominantly young (mean age 45 years overall) and male (76% overall). Six-month mortality was 22% for RTAs, 32% for falls and 17% for assault. Of survivors at 6 months with a known GOSE category, 44% had severe disability, 30% moderate disability and 26% made a good recovery. Overall, 61% of patients with known outcome had an unfavourable outcome (death or severe disability) at 6 months. Between 35% and 70% of survivors reported problems across the five domains of the EQ-5D-3L. Of the 10 risk models selected for validation, the best discrimination overall was from the International Mission for Prognosis and Analysis of Clinical Trials in TBI Lab model (IMPACT) (c-index 0.779 for mortality, 0.713 for unfavourable outcome). The model was well calibrated for 6-month mortality but substantially underpredicted the risk of unfavourable outcome at 6 months. Baseline patient characteristics were similar between dedicated neurocritical care units and combined neuro/general critical care units. In lifetime cost-effectiveness analysis, dedicated neurocritical care units had higher mean lifetime quality-adjusted life-years (QALYs) at small additional mean costs with an incremental cost-effectiveness ratio (ICER) of £14,000 per QALY and incremental net monetary benefit (INB) of £17,000. The cost-effectiveness acceptability curve suggested that the probability that dedicated compared with combined neurocritical care units are cost-effective is around 60%. There were substantial differences in case mix between the 'early' (within 18 hours of presentation) and 'no or late' (after 24 hours) transfer groups. After adjustment, the 'early' transfer group reported higher lifetime QALYs at an additional cost with an ICER of £11,000 and INB of £17,000. CONCLUSIONS: The risk models demonstrated sufficient statistical performance to support their use in research but fell below the level required to guide individual patient decision-making. The results suggest that management in a dedicated neurocritical care unit may be cost-effective compared with a combined neuro/general critical care unit (although there is considerable statistical uncertainty) and support current recommendations that all patients with severe TBI would benefit from transfer to a neurosciences centre, regardless of the need for surgery. We recommend further research to improve risk prediction models; consider alternative approaches for handling unobserved confounding; better understand long-term outcomes and alternative pathways of care; and explore equity of access to postcritical care support for patients following acute TBI. FUNDING: The National Institute for Health Research Health Technology Assessment programme

Gluten-free dough-making of specialty breads: Significance of blended starches, flours and additives on dough behaviour

The capability of different gluten-free (GF) basic formulations made of flour (rice, amaranth and chickpea) and starch (corn and cassava) blends, to make machinable and viscoelastic GF-doughs in absence/presence of single hydrocolloids (guar gum, locust bean and psyllium fibre), proteins (milk and egg white) and surfactants (neutral, anionic and vegetable oil) have been investigated. Macroscopic (high deformation) and macromolecular (small deformation) mechanical, viscometric (gelatinization, pasting, gelling) and thermal (gelatinization, melting, retrogradation) approaches were performed on the different matrices in order to (a) identify similarities and differences in GF-doughs in terms of a small number of rheological and thermal analytical parameters according to the formulations and (b) to assess single and interactive effects of basic ingredients and additives on GF-dough performance to achieve GF-flat breads. Larger values for the static and dynamic mechanical characteristics and higher viscometric profiles during both cooking and cooling corresponded to doughs formulated with guar gum and Psyllium fibre added to rice flour/starch and rice flour/corn starch/chickpea flour, while surfactant- and protein-formulated GF-doughs added to rice flour/starch/amaranth flour based GF-doughs exhibited intermediate and lower values for the mechanical parameters and poorer viscometric profiles. In addition, additive-free formulations exhibited higher values for the temperature of both gelatinization and retrogradation and lower enthalpies for the thermal transitions. Single addition of 10% of either chickpea flour or amaranth flour to rice flour/starch blends provided a large GF-dough hardening effect in presence of corn starch and an intermediate effect in presence of cassava starch (chickpea), and an intermediate reinforcement of GF-dough regardless the source of starch (amaranth). At macromolecular level, both chickpea and amaranth flours, singly added, determined higher values of the storage modulus, being strengthening effects more pronounced in presence of corn starch and cassava starch, respectively.The authors acknowledge the financial support of Regione Autonoma della Sardegna, Legge 7, project title “Ottimizzazione della formulazione e della tecnologia di processo per la produzione di prodotti da forno gluten-free fermentati e non fermentati” and Spanish institutions Consejo Superior de Investigaciones Científicas (CSIC) and Ministerio de Economía y Competitividad (Project AGL2011-22669).Peer Reviewe

Delineating vineyard zones by fuzzy K-means algorithm based on grape sampling variables

[EN] This study describes a method for delineating management zones using interpolated maps of grape characteristics recorded in 2013 and 2014 in a Godello vineyard located in the Bierzo Denomination of Origin (León, Northwest Spain). Ten variables were analyzed and recorded for the sampled vines (50 vines/ha). Interpolated maps reflecting each variable and year were created by spatial interpolation (kriging) from the sampled points. Principal component analysis was used to detect relationships between variables and to select the variables to be used to create the cluster classification. Using the fuzzy k-means classification algorithm implemented in the Management Zone Analyst (MZA v.1.0.0) software, several zones were delineated by combining the studied variables. The results delineated 2 different management areas composed of 3 zones each based on winery objectives: (1) to increase grape production (combining the yield for 2013 and 2014); and (2) to improve grape composition (combining the pH for 2013 and 2014).SIThis work was supportedby the Universidad de León, Spain [grant number 2016/00145/001-T102]. The authors acknowledge the assistance of the Bodegas y Viñedos Bergidenses, SAT. suppor

An evaluation of the clinical and cost-effectiveness of alternative care locations for critically ill adult patients with acute traumatic brain injury.

BACKGROUND: For critically ill adult patients with acute traumatic brain injury (TBI), we assessed the clinical and cost-effectiveness of: (a) Management in dedicated neurocritical care units versus combined neuro/general critical care units within neuroscience centres. (b) 'Early' transfer to a neuroscience centre versus 'no or late' transfer for those who present at a non-neuroscience centre. METHODS: The Risk Adjustment In Neurocritical care (RAIN) Study included prospective admissions following acute TBI to 67 UK adult critical care units during 2009-11. Data were collected on baseline case-mix, mortality, resource use, and at six months, Glasgow Outcome Scale Extended (GOSE), and quality of life (QOL) (EuroQol 5D-3L). We report incremental effectiveness, costs and cost per Quality-Adjusted Life Year (QALY) of the alternative care locations, adjusting for baseline differences with validated risk prediction models. We tested the robustness of results in sensitivity analyses. FINDINGS: Dedicated neurocritical care unit patients (N = 1324) had similar six-month mortality, higher QOL (mean gain 0.048, 95% CI -0.002 to 0.099) and increased average costs compared with those managed in combined neuro/general units (N = 1341), with a lifetime cost per QALY gained of £14,000. 'Early' transfer to a neuroscience centre (N = 584) was associated with lower mortality (odds ratio 0.52, 0.34-0.80), higher QOL for survivors (mean gain 0.13, 0.032-0.225), but positive incremental costs (£15,001, £11,123 to £18,880) compared with 'late or no transfer' (N = 263). The lifetime cost per QALY gained for 'early' transfer was £11,000. CONCLUSIONS: For critically ill adult patients with acute TBI, within neuroscience centres management in dedicated neurocritical care units versus combined neuro/general units led to improved QoL and higher costs, on average, but these differences were not statistically significant. This study finds that 'early' transfer to a neuroscience centre is associated with reduced mortality, improvement in QOL and is cost-effective