SUNNY with Algorithm Configuration

International audienceThe SUNNY algorithm is a portfolio technique originally tailored for Constraint Satisfaction Problems (CSPs). SUNNY allows to select a set of solvers to be run on a given CSP, and was proven to be effective in the MiniZinc Challenge, i.e., the yearly international competition for CP solvers. In 2015, SUNNY was compared with other solver selectors in the first ICON Challenge on algorithm selection with less satisfactory performance. In this paper we briefly describe the new version of the SUNNY approach for algorithm selection, that was submitted to the first Open Algorithm Selection Challenge

sunny-as2: Enhancing SUNNY for Algorithm Selection

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC

Bayesian model selection in logistic regression for the detection of adverse drug reactions

Motivation: Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, exploring such databases requires statistical methods. In this context, disproportionality measures are used. However, by projecting the data onto contingency tables, these methods become sensitive to the problem of co-prescriptions and masking effects. Recently, logistic regressions have been used with a Lasso type penalty to perform the detection of associations between drugs and adverse events. However, the choice of the penalty value is open to criticism while it strongly influences the results. Results: In this paper, we propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. Thus, we avoid the calibration of penalty or threshold. During our application on the French pharmacovigilance database, the proposed method is compared to well established approaches on a reference data set, and obtains better rates of positive and negative controls. However, many signals are not detected by the proposed method. So, we conclude that this method should be used in parallel to existing measures in pharmacovigilance.Comment: 7 pages, 3 figures, submitted to Biometrical Journa

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets

Introduction Predictive survival modeling offers systematic tools for clinical decision-making and individualized tailoring of treatment strategies to improve patient outcomes while reducing overall healthcare costs. In 2015, a number of machine learning and statistical models were benchmarked in the DREAM 9.5 Prostate Cancer Challenge, based on open clinical trial data for metastatic castration resistant prostate cancer (mCRPC). However, applying these models into clinical practice poses a practical challenge due to the inclusion of a large number of model variables, some of which are not routinely monitored or are expensive to measure. Objectives To develop cost-specified variable selection algorithms for constructing cost-effective prognostic models of overall survival that still preserve sufficient model performance for clinical decision making. Methods Penalized Cox regression models were used for the survival prediction. For the variable selection, we implemented two algorithms: (i) LASSO regularization approach; and (ii) a greedy cost-specified variable selection algorithm. The models were compared in three cohorts of mCRPC patients from randomized clinical trials (RCT), as well as in a real-world cohort (RWC) of advanced prostate cancer patients treated at the Turku University Hospital. Hospital laboratory expenses were utilized as a reference for computing the costs of introducing new variables into the models. Results Compared to measuring the full set of clinical variables, economic costs could be reduced by half without a significant loss of model performance. The greedy algorithm outperformed the LASSO-based variable selection with the lowest tested budgets. The overall top performance was higher with the LASSO algorithm. Conclusion The cost-specified variable selection offers significant budget optimization capability for the real-world survival prediction without compromising the predictive power of the model.Peer reviewe

Video Game AI Algorithms

The ubiquity of human-like characters in video games presents the challenge of implementing human-like behaviors. To address the pathfinding and behavior selection problems faced in a real project, we came up with two improved methods based upon mainstream solutions. To make pathfinding agent take into account more incentives than only a destination, We designed a new pathfinding algorithm named Cost Radiation A* (CRA*), based on the A* heuristic search algorithm. CRA* incorporates the agent\u27s preference for other objects, represented as cost radiators in our scheme. We also want to enable non-player characters (NPCs) to learn in real-time in response to a player\u27s actions. We adopt the behavior tree framework, and design a new composite node for it, named learner node, which enables developers to design learning behaviors. The learner node achieves basic reinforcement learning but is also open to more sophisticated use

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

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

ANALYZING BIG DATA WITH DECISION TREES

ANALYZING BIG DATA WITH DECISION TREE

Selection of EAP-authentication methods in WLANs

IEEE 802.1X is a key part of IEEE802.11i. By employing Extensible Authentication Protocol (EAP) it supports a variety of upper layer authentication methods each with different benefits and drawbacks. Any one of these authentication methods can be the ideal choice for a specific networking environment. The fact that IEEE 802.11i leaves the selection of the most suitable authentication method to system implementers makes the authentication framework more flexible, but on the other hand leads to the question of how to select the authentication method that suits an organisation’s requirements and specific networking environment. This paper gives an overview of EAP authentication methods and provides a table comparing their properties. It then identifies the crucial factors to be considered when employing EAP authentication methods in WLAN environments. The paper presents algorithms that guide the selection of an EAP-authentication method for a WLAN and demonstrates their application through three examples