Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach

In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF, which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases

An outlier ranking tree selection approach to extreme pruning of random forests.

Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance in terms of predictive accuracy. This explains why, over the past decade, there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, the objective of this paper is twofold. First, it investigates how an unsupervised learning technique, namely, Local Outlier Factor (LOF) can be used to identify diverse trees in the RF. Second, trees with the highest LOF scores are then used to create a new RF termed LOFB-DRF that is much smaller in size than RF, and yet performs at least as good as RF, but mostly exhibits higher performance in terms of accuracy. The latter refers to a known technique called ensemble pruning. Experimental results on 10 real datasets prove the superiority of our proposed method over the traditional RF. Unprecedented pruning levels reaching as high as 99% have been achieved at the time of boosting the predictive accuracy of the ensemble. The notably extreme pruning level makes the technique a good candidate for real-time applications

Ensemble based Clustering of Plasmodium falciparum genes

Ensemble learning is a recent and extended approach to the unsupervised data mining technique called clustering which is used from finding natunl gmupings that exist in a dataset. Hetre, we applied an ensemble based clustering algol'ithm called Random Fot·ests with Pat·tition amund Medoids (PAM) to multiple time sel'ies gene expt·ession data of Plasmodium falcipat·um. The Random Fot·est algol'ithm is most common ensemble leat·ning appmach that uses decision tt·ees. Random Fm·est consists of lat·ge numbet· of classification tt·ees (nnging fmm hundt·eds to thousands) built from rabootstnp sampling of the dataset. We also applied the following intemal clustet· validity measures; Silhouette Width index, Connectivity Index and the Dunn Index to select the optimal numbet· of final clustet·s. Om· t·esults show that ensemble based clustering is indeed a good altet·native fm· clustet· analysis with the premise of an improved performance ovet· traditional clustering algorithm

Reimagining the SSMinT Software Package

We examine two proposed indexing algorithms taking advantage of the new SSMinT libraries. The two algorithms primarily differ in their selection of documents for learning. The batch indexing method selects some random number of documents for learning. The iterative indexing method uses a single randomly selected document to discover semantic signatures, which are then used to find additional related documents. The batch indexing method discovers one to three semantic signatures per document, resulting in poor clustering performance as evaluated by human cross-validation of clusters using the Adjusted Rand Index. The iterative indexing method discovers more semantic signatures per document, resulting in far better clustering performance using the same cross-validation method.;Our new tools enable faster development of new experiments, forensic applications, and more. The experiments show that SSMinT can provide effective indexing for text data such as e-mail or web pages. We conclude with areas of future research which may benefit from utilizing SSMinT. (Abstract shortened by ProQuest.)

Robust Entity Linking in Heterogeneous Domains

Entity Linking is the task of mapping terms in arbitrary documents to entities in a knowledge base by identifying the correct semantic meaning. It is applied in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question and Answering. Most existing Entity Linking systems were optimized for specific domains (e.g., general domain, biomedical domain), knowledge base types (e.g., DBpedia, Wikipedia), or document structures (e.g., tables) and types (e.g., news articles, tweets). This led to very specialized systems that lack robustness and are only applicable for very specific tasks. In this regard, this work focuses on the research and development of a robust Entity Linking system in terms of domains, knowledge base types, and document structures and types. To create a robust Entity Linking system, we first analyze the following three crucial components of an Entity Linking algorithm in terms of robustness criteria: (i) the underlying knowledge base, (ii) the entity relatedness measure, and (iii) the textual context matching technique. Based on the analyzed components, our scientific contributions are three-fold. First, we show that a federated approach leveraging knowledge from various knowledge base types can significantly improve robustness in Entity Linking systems. Second, we propose a new state-of-the-art, robust entity relatedness measure for topical coherence computation based on semantic entity embeddings. Third, we present the neural-network-based approach Doc2Vec as a textual context matching technique for robust Entity Linking. Based on our previous findings and outcomes, our main contribution in this work is DoSeR (Disambiguation of Semantic Resources). DoSeR is a robust, knowledge-base-agnostic Entity Linking framework that extracts relevant entity information from multiple knowledge bases in a fully automatic way. The integrated algorithm represents a collective, graph-based approach that utilizes semantic entity and document embeddings for entity relatedness and textual context matching computation. Our evaluation shows, that DoSeR achieves state-of-the-art results over a wide range of different document structures (e.g., tables), document types (e.g., news documents) and domains (e.g., general domain, biomedical domain). In this context, DoSeR outperforms all other (publicly available) Entity Linking algorithms on most data sets

Accuracy and Diversity in Ensembles of Text Categorisers

Error-Correcting Out Codes (ECOC) ensembles of binary classifiers are used in Text Cate- gorisation to improve the accuracy while benefiting from learning algorithms that only support two classes. An accurate ensemble relies on the quality of its corresponding decomposition ma- trix, which at the same time depends on the separation between the categories and the diversity of the dichotomies representing the binary classifiers. Important open questions include finding a good definition for diversity between two dichotomies and a way of combining all the pairwise diversity values into a single indicator that we call the decomposition quality. In this work we introduce a new measure to estimate the diversity between two learners and we compare it to the well-known Hamming distance. We also examine three functions to evaluate the decomposition quality. We present a set of experiments where these measures and functions are tested using two distinct document corpora with several configurations in each. The analysis of the results shows a weak relationship between the ensemble accuracy and its diversity