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With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.

We present a new variant of the Vehicle Routing Problem based on a real industrial scenario. This VRP is dynamic and heavily constrained and uses time-windows, a heterogeneous vehicle fleet and multiple types of job. A constructive solver is developed and tested using dynamic simulation of real-world data from a leading Scottish haulier. Our experiments establish the efficiency and reliability of the method for this problem. Additionally, a methodology for evaluating policy changes through simulation is presented, showing that our technique supports operations and management. We establish that fleet size can be reduced or more jobs handled by the company.

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO.

Ensemble selection is one of the most studied topics in ensemble learning because a selected subset of base classifiers may perform better than the whole ensemble system. In recent years, a great many ensemble selection methods have been introduced. However, many of these lack flexibility: either a fixed subset of classifiers is pre-selected for all test samples (static approach), or the selection of classifiers depends upon the performance of techniques that define the region of competence (dynamic approach). In this paper, we propose an ensemble selection method that takes into account each base classifier's confidence during classification and the overall credibility of the base classifier in the ensemble. In other words, a base classifier is selected to predict for a test sample if the confidence in its prediction is higher than its credibility threshold. The credibility thresholds of the base classifiers are found by minimizing the empirical 0–1 loss on the entire training observations. In this way, our approach integrates both the static and dynamic aspects of ensemble selection. Experiments on 62 datasets demonstrate that the proposed method achieves much better performance in comparison to some ensemble methods.

Objectives: Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Partin tables to support their decisions. However, Partin tables are based on logistic regression (LR) and built from US data. Our objective is to investigate a range of both predictive methods and of predictive variables for pathological stage prediction and assess them with respect to their predictive quality based on UK data. Methods and material: The latest version of Partin tables was applied to a large scale British dataset in order to measure their performances by mean of concordance index (c-index). The data was collected by the British Association of Urological Surgeons (BAUS) and gathered records from over 1700 patients treated with prostatectomy in 57 centers across UK. The original methodology was replicated using the BAUS dataset and evaluated using concordance index. In addition, a selection of classifiers, including, among others, LR, artificial neural networks and Bayesian networks (BNs) was applied to the same data and compared with each other using the area under the ROC curve (AUC). Subsets of the data were created in order to observe how classifiers perform with the inclusion of extra variables. Finally a local dataset prepared by the Aberdeen Royal Infirmary was used to study the effect on predictive performance of using different variables. Results: Partin tables have low predictive quality (c-index=0.602) when applied on UK data for comparison on patients with organ confined and extra prostatic extension conditions, patients at the two most frequently observed pathological stages. The use of replicate lookup tables built from British data shows an improvement in the classification, but the overall predictive quality remains low (c-index=0.610). Comparing a range of classifiers shows that BNs generally outperform other methods. Using the four variables from Partin tables, naive Bayes is the best classifier for the prediction of each class label (AUC=0.662 for OC). When two additional variables are added, the results of LR (0.675), artificial neural networks (0.656) and BN methods (0.679) are overall improved. BNs show higher AUCs than the other methods when the number of variables raises Conclusion: The predictive quality of Partin tables can be described as low to moderate on UK data. This means that following the predictions generated by Partin tables, many patients would received an inappropriate treatment, generally associated with a deterioration of their quality of life. In addition to demographic differences between UK and the original US population, the methodology and in particular LR present limitations. BN represents a promising alternative to LR from which prostate cancer staging can benefit. Heuristic search for structure learning and the inclusion of more variables are elements that further improve BN models quality.

Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.

Cefprozil is a beta-lactamase-stable oral cephalosporin with an antimicrobial spectrum that includes gram-positive and gram-negative pathogens commonly associated with acute bacterial sinusitis, one of the most common upper respiratory tract infections among adults. We conducted a multicenter, open-label study to compare the efficacy and safety of cefprozil and amoxicillin/clavulanate in the treatment of adults with severe acute bacterial sinusitis diagnosed by clinical and radiographic criteria. A total of 278 patients entered the study, 140 (59 males, 81 females) in the cefprozil group and 138 (69 males, 69 females) in the amoxicillin/clavulanate group. Patients were randomized to 10 days of treatment with either cefprozil 500 mg BID or amoxicillin/clavulanate 500 mg/125 mg TID. Clinical severity was assessed at study entry, and patients were stratified based on symptom grade. Efficacy was evaluated using a 10-point questionnaire administered during, at the end of, and 2 weeks after completing therapy. At the end of treatment, 84.5% (71/84) of patients with severe sinusitis treated with cefprozil had a satisfactory clinical response, which was not significantly different from the 89.9% (80/89) of patients in the amoxicillin/clavulanate group who had a satisfactory clinical response. Two weeks after completing treatment, 80.8% (63/78) of cefprozil-treated patients and 81.0% (64/79) of amoxicillin/clavulanate-treated patients with severe sinusitis had a satisfactory response. Relapse was more common among amoxicillin/clavulanate patients (6/70; 8.6%) than among cefprozil patients (2/65; 3.1%), but the difference was not statistically significant. Significantly more amoxicillin/clavulanate-treated patients experienced adverse events compared with cefprozil-treated patients (P < 0.001), including diarrhea (P < 0.001), nausea (P < 0.042), and rash (P < 0.035). Three times as many amoxicillin/clavulanate patients discontinued treatment because of adverse events. Cefprozil demonstrated comparable clinical efficacy to amoxicillin/clavulanate in the treatment of adults with severe sinusitis; however, cefprozil was associated with a significantly lower incidence of diarrhea, nausea, and rash.

A JAVA application, The Oncologists Workbench, which allows oncologists to estimate the influence of new cancer treatment schedules is being developed. The requirement for a rational approach to the design of chemotherapeutic regimens is well established [1]. Our prototype allows oncologists using the World Wide Web (WWW) to graphically construct treatment regimens while considering various toxic side effects. A simulation engine makes predictions of tumour growth based on previous clinical knowledge of response to treatment. The oncologist can then examine the predicted tumour response information with a specially constructed interactive viewer. These interlinked tools allow oncologists to develop and predict the effectiveness of novel chemotherapeutic regimens. This work is part of an ongoing collaboration between oncologists, mathematicians and computer scientists to provide tools for improving cancer chemotherapy.