Random Prism: An Alternative to Random Forests.

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting

Blind prediction of protein B-factor and flexibility

Debye-Waller factor, a measure of X-ray attenuation, can be experimentally observed in protein X-ray crystallography. Previous theoretical models have made strong inroads in the analysis of B-factors by linearly fitting protein B-factors from experimental data. However, the blind prediction of B-factors for unknown proteins is an unsolved problem. This work integrates machine learning and advanced graph theory, namely, multiscale weighted colored graphs (MWCGs), to blindly predict B-factors of unknown proteins. MWCGs are local features that measure the intrinsic flexibility due to a protein structure. Global features that connect the B-factors of different proteins, e.g., the resolution of X-ray crystallography, are introduced to enable the cross-protein B-factor predictions. Several machine learning approaches, including ensemble methods and deep learning, are considered in the present work. The proposed method is validated with hundreds of thousands of experimental B-factors. Extensive numerical results indicate that the blind B-factor predictions obtained from the present method are more accurate than the least squares fittings using traditional methods.Comment: 5 figures, 23 page

Reduction of nitrogen oxides by injection of urea in the freeboard of a pilot scale fluidized bed combustor

The ‘thermal deNOx’ process using urea has been investigated in a 1 MW fluidized bed combustor. NOx reductions of up to 76% were obtainable by using this method. The experimental results show that urea is at least as active as NH3, which is commonly used in this application, but which is far more toxic and corrosive. Emission levels of 200 mg m−3 for NOx could be achieved by injecting the urea at a height of 2 m above the distribution plate in a molar ratio urea:NOx = 1.5. The SO2 emission value also appeared to be reduced when the urea was injected at a urea: NOx molar ratio > 4

A rule-based classifier with accurate and fast rule term induction for continuous attributes

Rule-based classifiers are considered more expressive, human readable and less prone to over-fitting compared with decision trees, especially when there is noise in the data. Furthermore, rule-based classifiers do not suffer from the replicated subtree problem as classifiers induced by top down induction of decision trees (also known as `Divide and Conquer'). This research explores some recent developments of a family of rule-based classifiers, the Prism family and more particular G-Prism-FB and G-Prism-DB algorithms, in terms of local discretisation methods used to induce rule terms for continuous data. The paper then proposes a new algorithm of the Prism family based on a combination of Gauss Probability Density Distribution (GPDD), InterQuartile Range (IQR) and data transformation methods. This new rule-based algorithm, termed G-Rules-IQR, is evaluated empirically and outperforms other members of the Prism family in execution time, accuracy and tentative accuracy

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction

Improving modular classification rule induction with G-Prism using dynamic rule term boundaries

Modular classification rule induction for predictive analytics is an alternative and expressive approach to rule induction as opposed to decision tree based classifiers. Prism classifiers achieve a similar classification accuracy compared with decision trees, but tend to overfit less, especially if there is noise in the data. This paper describes the development of a new member of the Prism family, the G-Prism classifier, which improves the classification performance of the classifier. G-Prism is different compared with the remaining members of the Prism family as it follows a different rule term induction strategy. G-Prism’s rule term induction strategy is based on Gauss Probability Density Distribution (GPDD) of target classes rather than simple binary splits (local discretisation). Two versions of G-Prism have been developed, one uses fixed boundaries to build rule terms from GPDD and the other uses dynamic rule term boundaries. Both versions have been compared empirically against Prism on 11 datasets using various evaluation metrics. The results show that in most cases both versions of G-Prism, especially G-Prism with dynamic boundaries, achieve a better classification performance compared with Prism

Lack of evidence for central sensitization in idiopathic, non-traumatic neck pain : a systematic review

Background: Chronic neck pain is a common problem with a poorly understood pathophysiology. Often no underlying structural pathology can be found and radiological imaging findings are more related to age than to a patient's symptoms. Besides its common occurrence, chronic idiopathic neck pain is also very disabling with almost 50% of all neck pain patients showing moderate disability at long-term follow-up. Central sensitization (CS) is defined as "an amplification of neural signaling within the central nervous system that elicits pain hypersensitivity," "increased responsiveness of nociceptive neurons in the central nervous system to their normal or subthreshold afferent input," or "an augmentation of responsiveness of central neurons to input from unimodal and polymodal receptors." There is increasing evidence for involvement of CS in many chronic pain conditions. Within the area of chronic idiopathic neck pain, there is consistent evidence for the presence and clinical importance of CS in patients with traumatic neck pain, or whiplash-associated disorders. However, the majority of chronic idiopathic neck pain patients are unrelated to a traumatic injury, and hence are termed chronic idiopathic non-traumatic neck pain. When comparing whiplash with idiopathic non-traumatic neck pain, indications for different underlying mechanisms are found. Objective: The goal of this article was to review the existing scientific literature on the role of CS in patients with chronic idiopathic non-traumatic neck pain. Study Design: Systematic review. Setting: All selected studies were case control studies. Methods: A systematic search of existing, relevant literature was performed via the electronic databases Medline, Embase, Web of Science, Cinahl, PubMed, and Google Scholar. All titles and abstracts were checked to identify relevant articles. An article was considered eligible if it met following inclusion criteria: (1) participants had to be human adults (> 18 years) diagnosed with idiopathic non-traumatic chronic (present for at least 3 months) neck pain; (2) papers had to report outcomes related to CS; and (3) articles had to be full-text reports or original research (no abstracts, case-reports, reviews, meta-analysis, letters, or editorials). Results: Six articles were found eligible after screening the title, abstract and - when necessary the full text for in- and exclusion criteria. All selected studies were case-control studies. Overall, results regarding the presence of CS were divergent. While the majority of patients with chronic traumatic neck pain (i.e. whiplash) are characterized by CS, this is not the case for patients with chronic idiopathic neck pain. The available evidence suggests that CS is not a major feature of chronic idiopathic neck pain. Individual cases might have CS pain, but further work should reveal how they can be characterized. Limitations: Very few studies available. Conclusions: Literature about CS in patients with chronic idiopathic non-traumatic neck pain is rare and results from the available studies provide an inconclusive message. CS is not a characteristic feature of chronic idiopathic and non-traumatic neck pain, but can be present in some individuals of the population. In the future a subgroup with CS might be defined, but based on current knowledge it is not possible to characterize this subgroup. Such information is important in order to provide targeted treatment

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise