SUNNY-CP and the MiniZinc Challenge

In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the MiniZinc Challenge---the annual international competition for CP solvers---where it won two gold medals in 2015 and 2016. Under consideration in Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

Про уніфікацію способів обробки структурованої інформації

Розглядаються проблеми пошуку та збору інформації з однотипних DOM-моделей. Запропоновано механізм збору структурованої інформації з релевантних джерел, що дозволяє будувати універсальні аналізатори даних з цих джерел

AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)

Article in monograph or in proceedingsLeiden Inst Advanced Computer Science

AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)

Article in monograph or in proceedingsLeiden Inst Advanced Computer Science

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic modelling is frequently used as a text-mining tool for the discovery of hidden semantic structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used topic model that aims to explain the structure of a corpus by grouping texts. LDA requires multiple parameters to work well, and there are only rough and sometimes conflicting guidelines available on how these parameters should be set. In this paper, we contribute (i) a broad study of parameters to arrive at good local optima for GitHub and Stack Overflow text corpora, (ii) an a-posteriori characterisation of text corpora related to eight programming languages, and (iii) an analysis of corpus feature importance via per-corpus LDA configuration. We find that (1) popular rules of thumb for topic modelling parameter configuration are not applicable to the corpora used in our experiments, (2) corpora sampled from GitHub and Stack Overflow have different characteristics and require different configurations to achieve good model fit, and (3) we can predict good configurations for unseen corpora reliably. These findings support researchers and practitioners in efficiently determining suitable configurations for topic modelling when analysing textual data contained in software repositories.Comment: to appear as full paper at MSR 2019, the 16th International Conference on Mining Software Repositorie

Hybrid classification system design using a decision learning approach and three layered structure - A Meta learning paradigm in Data Mining

A data classification system is designed consisting of three layers. The second layer is the main focus of this research paper. It describes a meta-learning (learning to learn) concept that uses certain characteristics of the dataset as well as some more general knowledge about supervised and unsupervised machine learning algorithms (e.g. supervised learners tend to perform very well in the presence of a large pre-labelled training sets, etc.) to create some hypothesis. The main aim of this research is to harness general knowledge about a dataset and different machine learning methods to develop a set of meta-rules that when implemented will help to automate and speed up big data classification processes in data mining. An experiment is conducted to verify the hypotheses made using supervised and unsupervised knowledge flows in weka with some datasets taken from weka and UCI machine learning repositories. The performance result of the experiments is used to design a meta-learning algorithm in form of rules. The results from the experiments confirmed that general knowledge known about supervised and unsupervised learning is then harnessed successfully for making learning decisions

An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

In the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem. In this work we reproduce, simulate, and evaluate the performance of different portfolio approaches on extensive benchmarks of Constraint Satisfaction Problems. Empirical results clearly show the benefits of portfolio solvers in terms of both solved instances and solving time