In this paper a novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of the solutions in the search space is introduced, known as ...extended Deep Statistical Comparison. This approach is an extension of the recently proposed Deep Statistical Comparison approach used for comparing meta-heuristic stochastic optimization algorithms according to the solutions values. Its main contribution is that the algorithms are compared not only according to obtained solutions values, but also according to the distribution of the obtained solutions in the search space. The information it provides can additionally help to identify exploitation and exploration powers of the compared algorithms. This is important when dealing with a multimodal search space, where there are a lot of local optima with similar values. The benchmark results show that our proposed approach gives promising results and can be used for a statistical comparison of meta-heuristic stochastic optimization algorithms according to solutions values and their distribution in the search space.
Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly ...published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In benchmarking theory, creating a comprehensive and uniformly distributed set of problems is a crucial first step to designing a good benchmark. However, this step is also one of the hardest, as it ...can be difficult to determine how to evaluate the quality of the chosen problem set.
In this article, we evaluate if the field of exploratory landscape analysis can be used to develop a generalized method of visualizing a set of arbitrary optimization functions. We present a method for visually determining the distribution of problems within a benchmark set using exploratory landscape analysis combined with clustering and t-sne visualization, and evaluate and explain the visualization this methodology produces.
The proposed method is evaluated on a set of benchmark problems taken from two well known state-of-the-art real-parameter single objective optimization benchmarks: the CEC Special Sessions and Competitions on Real-Parameter Single Objective optimization, and the GECCO Black-Box Optimization Benchmark workshops.
The main goal of this paper is to present an analysis of how exploratory landscape analysis can be used to visualize a benchmark problem set. We show that this method can provide a clear visualization of a benchmark problem set and shows the similarities of the problems in it by placing similar problems visually close together. We also show that the problem sets of the above benchmarks have a somewhat distinct set of problems that do not overlap.
In addition, by applying feature selection approaches we show that a number of landscape features provided by state-of-the-art exploratory landscape analysis libraries are redundant and that a large amount of them are not invariant to simple transforms like scaling and shifting, at least when analyzing these two datasets.
DSCTool is a statistical tool for comparing performance of stochastic optimization algorithms on a single benchmark function (i.e. single-problem analysis) or a set of benchmark functions (i.e., ...multiple-problem analysis). DSCTool implements a recently proposed approach, called Deep Statistical Comparison (DSC), and its variants. DSC ranks optimization algorithms by comparing distributions of obtained solutions for a problem instead of using a simple descriptive statistic such as the mean or the median. The rankings obtained for an individual problem give the relations between the performance of the applied algorithms. To compare optimization algorithms in the multiple-problem scenario, an appropriate statistical test must be applied to the rankings obtained for a set of problems. The main advantage of DSCTool are its REST web services, which means all its functionalities can be accessed from any programming language. In this paper, we present the DSCTool in detail with examples for its usage.
•DSCTool - a statistical tool for comparing stochastic optimization algorithms.•REST web services implementation allows access from any programming language.•Identification of statistical and practical significance.•Understanding of exploitation and exploration powers of single-objective algorithms.•Ranking of multi-objective algorithms using an ensemble of quality indicators.
In this paper, we propose an extension of a recently proposed Deep Statistical Comparison (DSC) approach, called practical Deep Statistical Comparison (pDSC), which takes into account practical ...significance when making a statistical comparison of meta-heuristic stochastic optimization algorithms for single-objective optimization. For achieving practical significance, two variants of the standard DSC ranking scheme are proposed. The first is called sequential pDSC, and takes into account practical significance by preprocessing of the independent optimization runs in a sequential order. The second is called Monte Carlo pDSC, and avoids any dependency of practical significance with regard to the ordering of optimization runs. The analysis of identifying practical significance on benchmark tests for single-objective problems, shows that for some cases, both variants of pDSC compared to the Chess Rating System for Evolutionary Algorithms (CRS4EAs) approach give different conclusions. Preprocessing for practical significance is carried out in a similar way, but there are cases when the conclusion for practical significance differ, which comes from the different statistical concepts used to identify practical significance.
•A statistical methodology for identifying practical significance.•Two ranking schemes working/dealing with practical significance.•The proposed methodology outperforming state-of-the-art approaches.
The association between Hymenoptera venom‐triggered anaphylaxis (HVA) and clonal mast cell‐related disorders (cMCD) has been known for decades. However, recent breakthroughs in peripheral blood ...screening for KIT p.D816V missense variant have revealed the true extent of this clinical association whilst adding to our understanding of the underlying aetiology. Thus, recent large studies highlighted the presence of KIT p.D816V among 18.2% and 23% of patients with severe Hymenoptera venom‐triggered anaphylaxis. A significant proportion of those patients have normal serum basal tryptase (BST) levels, with no cutaneous findings such as urticaria pigmentosa or other systemic findings such as organomegaly that would have suggested the presence of cMCD. These findings of an increased prevalence suggest that the impact of cMCD on anaphylaxis could be clinically underestimated and that the leading question for clinicians could be changed from ‘how many patients with cMCD have anaphylaxis?’ to ‘how many patients with anaphylaxis have cMCD?’. The discovery of hereditary α‐tryptasemia (HαT)—a genetic trait caused by an increased copy number of the Tryptase Alpha/Beta 1 (TPSAB1) gene‐, first described in 2016, is now known to underlie the majority of cases of elevated BST outside of cMCD and chronic kidney disease. HαT is the first common heritable genetic modifier of anaphylaxis described, and it is associated with increased risk for severe HVA (relative risk = 2.0), idiopathic anaphylaxis, and an increased prevalence of anaphylaxis in patients with cMCD, possibly due to the unique activity profile of α/β ‐tryptase heterotetramers that may potentiate immediate hypersensitivity reaction severity. Our narrative review aims to highlight recent research to have increased our understanding of cMCD and HαT, through recent lessons learned from studying their association with HVA. Additionally, we examined the studies of mast cell‐related disorders in food and drug allergy in an effort to determine whether one should also consider cMCD and/or HαT in cases of severe anaphylaxis triggered by food or drugs.
Non‐clonal mast cell disease, hereditary alpha‐tryptasemia, and anaphylaxis. Hereditary alpha‐tryptasemia (HαT) is strongly associated with clonal mast cell disease (cMCD). Either of those conditions alone is a predisposing factor for severe IgE‐dependent and IgE‐independent anaphylaxis however, the presence of HαT in patients with cMCD serves to further increase the severity of anaphylactic reactions.
In this paper a novel approach for making a statistical comparison of meta-heuristic stochastic optimization algorithms over multiple single-objective problems is introduced, where a new ranking ...scheme is proposed to obtain data for multiple problems. The main contribution of this approach is that the ranking scheme is based on the whole distribution, instead of using only one statistic to describe the distribution, such as average or median. Averages are sensitive to outliers (i.e., the poor runs of the stochastic optimization algorithms) and consequently medians are sometimes used. However, using the common approach with either averages or medians, the results can be affected by the ranking scheme that is used by some standard statistical tests. This happens when the differences between the averages or medians are in some ϵ-neighborhood and the algorithms obtain different ranks though they should be ranked equally given the small differences that exist between them. The experimental results obtained on Black-Box Benchmarking 2015, show that our approach gives more robust results compared to the common approach in cases when the results are affected by outliers or by a misleading ranking scheme.
In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the ...problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used to train and test the model, and existing optimization benchmarks only provide a limited amount of data. To help with this problem, artificial problem generation has been shown to be a useful tool for augmenting existing benchmark problems. In this paper, we are interested in the problem of knowledge transfer between the artificially generated and existing handmade benchmark problems in the domain of continuous numerical optimization. That is, can an algorithm selection model trained purely on artificially generated problems correctly provide algorithm recommendations for existing handmade problems. We show that such a model produces low-quality results, and we also provide explanations about how the algorithm selection model works and show the differences between the problem data sets in order to explain the model’s performance.
Making a statistical comparison of meta-heuristic multi-objective optimization algorithms is crucial for identifying the strengths and weaknesses of a newly proposed algorithm. Currently, ...state-of-the-art comparison approaches involve user-preference-based selection of a single quality indicator or an ensemble of quality indicators as a comparison metric. Using these quality indicators, high-dimensional data is transformed into one-dimensional data. By doing this, information contained in the high-dimensional space can be lost, which will affect the results of the comparison. To avoid losing this information, we propose a novel ranking scheme that compares the distributions of high-dimensional data. Experimental results show that the proposed approach reduces potential information loss when statistical significance is not observed in high-dimensional data. Consequently, the selection of a quality indicator is required only in cases when statistical significance is observed in high-dimensional data. With this the cases that are affected by the user preference selection are reduced.
When making statistical analysis of single-objective optimization algorithms’ performance, researchers usually estimate it according to the obtained optimization results in the form of ...minimal/maximal values. Though this is a good indicator about the performance of the algorithm, it does not provide any information about the reasons why it happens. One possibility to get additional information about the performance of the algorithms is to study their exploration and exploitation abilities. In this paper, we present an easy-to-use step by step pipeline that can be used for performing exploration and exploitation analysis of single-objective optimization algorithms. The pipeline is based on a web-service-based e-Learning tool called DSCTool, which can be used for making statistical analysis not only with regard to the obtained solution values but also with regard to the distribution of the solutions in the search space. Its usage does not require any special statistic knowledge from the user. The gained knowledge from such analysis can be used to better understand algorithm’s performance when compared to other algorithms or while performing hyperparameter tuning.
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