N-gram and repeating pattern based prediction rules have been used for next-web request prediction. However, there is no rigorous study of how to select the best rule for a given observation. The longer patter may not be the best pattern, because such patters are also more rare. In this paper, we propose several rule-pruning methods that enable us to build efficient, compact and high-quality cl...

This paper describes a rule-learning approach towards Chinese prosodic phrase prediction for TTS systems. Firstly, we prepared a speech corpus having about 3000 sentences and manually labelled the sentences with two-level prosodic structure. Secondly, candidate features related to prosodic phrasing and the corresponding prosodic boundary labels are extracted from the corpus text to establish an...

Software defect prediction, if is effective, enables the developers to distribute their testing efforts efficiently and let them focus on defect prone modules. It would be very resource consuming to test all the modules while the defect lies in fraction of modules. Information about fault-proneness of classes and methods can be used to develop new strategies which can help mitigate the overall ...

Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to...

BACKGROUND Clostridium difficile infection poses a significant healthcare burden. However, the derivation of a simple, evidence based prediction rule to assist patient management has not yet been described. METHOD Univariate, multivariate and decision tree procedures were used to deduce a prediction rule from over 186 variables; retrospectively collated from clinical data for 213 patients. Th...

This Paper Focuses on Analyzing Medical Data using Association Rule mining for Heart Attack Prediction. Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that finds interesting association or correlation relation ship among a large set of data items and predicts the associative and correlative behaviors for new data. The Association rule mi...

The current authors developed a new prediction rule based on the five risk classes defined by the Pneumonia Severity Index to identify allocation of inpatient care in community-acquired pneumonia. The decision to hospitalise in low-risk classes (I-III) was unquestionable, if the presence of one or more of the following were evident: arterial oxygen tension <8.0 kPa (60 mmHg), shock, decompensat...

OBJECTIVE To determine predictive factors for the presence of focal infiltrates in children with clinically suspected pneumonia in a pediatric emergency department. METHODS Children (1-16 years) with clinically suspected pneumonia were studied prospectively. The presenting features were compared between the children with and without focal infiltrates using chi2 analysis, t test, and odds rati...

INTRODUCTION Early risk assessment is the mainstay of management of patients with sepsis. APACHE II is the gold standard prognostic stratification system. A prediction rule that aimed to improve prognostication by APACHE II with the application of serum suPAR (soluble urokinase plasminogen activator receptor) is developed. METHODS A prospective study cohort enrolled 1914 patients with sepsis ...

BACKGROUND To validate and update a prediction rule for estimating the risk of leprosy-related nerve function impairment (NFI). METHODOLOGY/PRINCIPAL FINDINGS Prospective cohort using routinely collected data, in which we determined the discriminative ability of a previously published rule and an updated rule with a concordance statistic (c). Additional risk factors were analyzed with a Cox p...