This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.
This is the first part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this first part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 on developing FCA-based methods for knowledge processing. We also give an overview of the literature on FCA extensions such as pattern structures, logical concept analysis, relational concept analysis, power context families, fuzzy FCA, rough FCA, temporal and triadic concept analysis and discuss scalability issues.
Although the multi-depot vehicle routing problem with simultaneous deliveries and pickups (MDVRPSDP) is often encountered in real-life scenarios of transportation logistics, it has received little attention so far. Particularly, no papers have ever used metaheuristics to solve it. In this paper a metaheuristic based on iterated local search is developed for MDVRPSDP. In order to strengthen the search, an adaptive neighborhood selection mechanism is embedded into the improvement steps and the perturbation steps of iterated local search, respectively. To diversify the search, new perturbation operators are proposed. Computational results indicate that the proposed approach outperforms the previous methods for MDVRPSDP. Moreover, when applied to VRPSDP benchmarks, the results are better than those obtained by large neighborhood search, particle swarm optimization, and ant colony optimization approach, respectively.
We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.
The paper deals with unconstrained face recognition task for the small sample size problem based on computation of distances between high-dimensional off-the-shelf features extracted by deep convolution neural network. We present the novel statistical recognition method, which maximizes the likelihood (joint probabilistic density) of the distances to all reference images from the gallery set. This likelihood is estimated with the known asymptotically normal distribution of the Kullback–Leibler discrimination between nonnegative features. Our approach penalizes the individuals if their feature vectors do not behave like the features of observed image in the space of dissimilarities of the gallery images. We provide the experimental study with the LFW (Labeled Faces in the Wild), YTF (YouTube Faces) and IJB-A (IARPA Janus Benchmark A) datasets and the state-of-the-art deep learning-based feature extractors (VGG-Face, VGGFace2, ResFace-101, CenterFace and Light CNN). It is demonstrated, that the proposed approach can be applied with traditional distances in order to increase accuracy in 0.3–5.5% when compared to known methods, especially if the training and testing images are significantly different.