Semi-Automated Identification of Ontological Labels in the Biomedical Literature with goldi

Recent growth in both the scale and the scope of large publicly available ontologies has spurred the development of computational methodologies which can leverage structured information to answer important questions. However, ontological labels, or “terms” have thus far proved difficult to use in practice; text mining, one crucial aspect of electronically understanding and parsing the biomedical literature, has historically had difficulty identifying “terms” in literature. In this article, we present goldi, an open source R package whose goal it is to identify terms of variable length in free form text. It is available at https://github.com/Chris1221/goldi or through CRAN. The algorithm works through identifying words or synonyms of words present in individual terms and comparing the number of present words to an acceptance function for decision making. In this article we present the theoretical rationale behind the algorithm, as well as practical advice for its usage applied to Gene Ontology term identification and quantification. We additionally detail the options available and describe their respective computational efficiencies.

A Recognition Method Using SA Optimized SVM-DS

Through properly setting the simulated annealing options of acceptance function, annealing function and temperature function, an adaptive hyper-parameter estimation method using simulated annealing algorithm is applied to improve the accuracy and efficiency of SVM. While, in order to eliminate the effects of error accumulation in multi-SVM, D-S theory is employed for decision fusion of SVM classifiers. When delimiting the belief and plausibility measures, recognition capability of SVM classifiers has been taken into account. And the Dempster decision rule also has been considered to the recognition result of each SVM classifier in the fusion algorithm. Finely, with the data set in the database of Statlog for the study, the experiment result indicates that this method can significantly increase the classification accuracy and demonstrate a good performance of robust.

KNOWLEDGE-BASED SELF-ADAPTATION IN EVOLUTIONARY SEARCH

Self-adaptation has been frequently employed in evolutionary computation. Angeline1 defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.

CAEP: AN EVOLUTION-BASED TOOL FOR REAL-VALUED FUNCTION OPTIMIZATION USING CULTURAL ALGORITHMS

Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process. The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.