Improved support vector machine generalization using normalized input space

Data pre-processing always plays a key role in learning algorithm performance. In this research we consider data pre-processing by normalization for Support Vector Machines (SVMs). We examine the normalization affect across 112 classification problems with SVM using the rbf kernel. We observe a significant classification improvement due to normalization. Finally we suggest a rule based method to find when normalization is necessary for a specific classification problem. The best normalization method is also automatically selected by SVM itself

On learning algorithm selection for classification

This paper introduces a new method for learning algorithm evaluation and selection, with empirical results based on classification. The empirical study has been conducted among 8 algorithms/classifiers with 100 different classification problems. We evaluate the algorithms’ performance in terms of a variety of accuracy and complexity measures. Consistent with the No Free Lunch theorem, we do not expect to identify the single algorithm that performs best on all datasets. Rather, we aim to determine the characteristics of datasets that lend themselves to superior modelling by certain learning algorithms. Our empirical results are used to generate rules, using the rule-based learning algorithm C5.0, to describe which types of algorithms are suited to solving which types of classification problems. Most of the rules are generated with a high confidence rating

Kernel width selection for SVM classification : a meta-learning approach

The most critical component of kernel-based learning algorithms is the choice of an appropriate kernel and its optimal parameters. In this paper, we propose a rule-based meta-learning approach for automatic radial basis function (RBF) kernel and its parameter selection for Support Vector Machine (SVM) classification. First, the best parameter selection is considered on the basis of prior information of the data with the help of Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. Then, the new rule-based meta-learning approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi-classclassification problems. We observe that our rule-based methodology provides significant improvement of computational time as well as accuracy in some specific cases

Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering

Over the last decade kernel based learning algorithms known as Support Vector Machines (SVMs) have become an attractive tool to solve pattern recognition problems. Choosing an appropriate kernel still is a trial and error approach for SVM however. This research provides some insights into the data characteristics that suit particular kernels. Our approach consists of four main stages. First, the performance of six kernels is examined across a collection of 33 classification problems from the machine learning literature. Secondly, a collection of statistics that describe each of the 33 problems in terms of data complexity is collected. After that, fuzzy C-means (FCM) is used to cluster, and construct a decision tree is used to generate the rules of the 33 problems based on these measurea of complexity. Each cluster represents a group of classification problems with similar data characteristics. The performance of each kernel within each cluster and the rules among the tree is then examined in the final stage to provide both quantitative and qualitative insights into which kernels perform best on certain problem types

Automatic parameter selection for polynomial kernel

Kernel is the heart of kernel based learning. To choose an appropriate parameter for a specific kernel is an important research issue in the data mining area. In this paper we propose an automatic parameter selection approach for polynomial kernel. The algorithm is tested on Support Vector Machines (SVM). The parameter selection is considered on the basis of prior information of the data distribution and Bayesian inference. The new approach is tested on different sizes of benchmark datasets with binary class problems as well as multi class classification problems

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings

Factors relating to engineering identity

Engineering identity is believed to relate to educational and professional persistence. In particular, a student’s sense of belonging to the engineering community is critical to that path. The primary research questions were: 1) which students self-identify as engineers?; and 2) what are the key factors that relate to self-identification? To address these research questions, a cross-sectional study of all undergraduate engineering students at a medium sized, Midwestern private university was conducted in the spring of 2009. The majority of engineering students did self-identify as engineers, with educational progression, gender and future career plans all being significant attributes. The factors that students most frequently identified as being necessary to be considered an engineer were intangible in nature and included: making competent design decisions, working with others to share ideas and accepting responsibility. Students’ self-identification as engineers can be linked to a sense of belonging to the engineering college, as well as organisational recognition

Evaluation of the 12-hour shift trial in a regional intensive care unit

Introduction: Given the shortage of critical care nurses, emphasis has been placed upon improving their working lives through the implementation of flexible work hours. Method: This descriptive exploratory study evaluated the effects of the implementation of the 12-hour roster in a regional intensive care unit (ICU). Staff (n = 19) completed a survey 12 weeks following the implementation of the 12-hour roster. Results: The study demonstrated widespread acceptance (92%) positive impact on physical and psychological well-being and increased work satisfaction (58%) for the nursing participants. Similarly, nurses working both the 8- and 12-hour rosters (75%), the doctors and allied health care workers all identified increased continuity of patient care as an outcome of the 12-hour shift. Participants strongly agreed that 12-hour rostering was a good recruitment (67%) and retention (75%) strategy. Conclusion: In an environment with considerable shortages of experienced critical care nurses, the use of flexible shift patterns such as the 12-hour roster was a positive recruitment and recruitment strategy

Delivering an empowerment intervention to a remote Indigenous child safety workforce: Its economic cost from an agency perspective

Background The Family Wellbeing (FWB) program applies culturally appropriate community led empowerment training to enhance the personal development of Aboriginal and Torres Strait Islander people in life skills. This study sought to estimate the economic cost required to deliver the FWB program to a child safety workforce in remote Australian communities. Method This study was designed as a retrospective cost description taken from the perspective of a non-government child safety agency. The target population were child protection residential care workers aged 24 or older, who worked in safe houses in five remote Indigenous communities and a regional office during the study year (2013). Resource utilization included direct costs (personnel and administrative) and indirect or opportunity costs of participants, regarded as absence from work. Results The total cost of delivering the FWB program for 66 participants was $182,588 ($2766 per participant), with 45% ($82,995) of costs classified as indirect (i.e., opportunity cost of participants time). Training cost could be further mitigated (∼30 Conclusion An investment of$2766 per participant created an opportunity to improve social and emotional wellbeing of remotely located workforce. This cost study provided policy relevant information by identifying the resources required to transfer the FWB program to other remote locations. It also can be used to support future comparative cost and outcome analyses and add to the evidence base around the cost-effectiveness of empowerment programs

Incidence and characteristics of ventilator-associated pneumonia in a regional non-tertiary Australian intensive care unit: protocol for a retrospective clinical audit study

INTRODUCTION: Ventilator-associated pneumonia (VAP) is a medical complication associated with prolonged mechanical ventilation. Most studies looking at VAP originate from major, tertiary intensive care units (ICUs). Our understanding of VAP in regional hospitals is limited. Given that patient characteristics often differ between metropolitan and regional centres, it is important to investigate VAP in a regional non-tertiary ICU. This project will establish and report the incidence, case characteristics and outcomes including mortality and length of stay related to VAP in a regional non-tertiary Australian ICU. Furthermore, it will compare the incidence of VAP in accordance with consultant diagnosed cases in the medical record, and by a post hoc screening of all cases against a list of previously published diagnostic criteria. METHODS AND ANALYSIS: This retrospective clinical audit study will screen medical records from the period 1 January 2013 to 31 December 2016. All cases requiring mechanical ventilation for ≥72 hours will be screened against previously reported diagnostic criteria for VAP. At the same time, their medical records will be screened for a documented diagnosis of VAP. ETHICS AND DISSEMINATION: This study has been granted ethical approval from the Central Queensland Hospital and Health Service (CQHHS) Human Research Ethics Committee (HREC/17/QCQ/11) and the Central Queensland University Human Research Ethics Committee (H17/05-102). This study will be submitted for publication in a peer-reviewed scientific journal and presented at internal workshops (within Queensland Health) and national and/or international scientific conferences