A group of fuzzy IF-THEN rules is belonging to one of the most popular, most effective, and user-friendliest knowledge representations. For this reason, extraction of these rules is becoming a more-and-more important part of the Data Mining stage in the Knowledge Discovery in Databases Process. In this paper, a direct algorithm for extracting fuzzy IF-THEN rules on the basis of linguistic variable elimination is described. The algorithm is implemented within a designed object-oriented software library Fuzzy Rule Miner. Besides the introduced algorithm, it implements two algorithms for fuzzy rule extraction based on using fuzzy decision trees of ID3 kind. An essential precondition for comparing the implemented algorithms and for verifying the legitimacy of the introduced algorithm is performance of experiments. The goal of experiments is to take in the behavior of algorithms on testing databases from the UCI Repository of Machine Learning Databases and to make comparisons of algorithms with one another. According to the conducted experiments, the introduced algorithm achieves high accuracy levels of discovered knowledge. The paper also contains a classification of rules and a specification of the Fuzzy Rule Discovery in Databases Process.
Alzheimer's Disease (AD) is the most frequent form of degenerative dementia and its early diagnosis is essential for effective treatment. Functional imaging modalities including Single Photon Emission Computed Tomography (SPECT) are often used with such an aim. However, conventional evaluation of SPECT images relies on manual reorientation and visual evaluation of tomographic slices which is time consuming, subjective and therefore prone to error. Our aim is to show an automatic Computer-Aided Diagnosis (CAD) system for improving the early detection of the AD. For this purpose, affine invariant descriptors of 3D SPECT image can be useful. The method consists of four steps: evaluation of invariant descriptors obtained using spherical harmonic analysis, statistical testing of their significance, application of regularized binary index models, and model verification via leave-one-out cross-validation scheme. The second approach is based on Support Vector Machine (SVM) classifier and visualization with use of self-organizing maps. Our approaches were tested on SPECT data from 11 adult patients with definite Alzheimer's disease and 10 adult patients with Amyotrophic Lateral Sclerosis (ALS) who were used as controls. A significant difference between SPECT spherical cuts of AD group and ALS group was both visually and numerically evaluated.
Recently, based on a limited morphological characterisation and partial 18S rRNA gene sequence, Jiang et al. (2019) described Trypanosoma micropteri Jiang, Lu, Du, Wang, Hu, Su et Li, 2019 as a new pathogen of farmed fish. Here we provide evidence based on the expanded sequence dataset, morphology and experimental infections that this trypanosome does not warrant the establishment as a new species, because it is conspecific with the long-term known Trypanosoma carassii Mitrophanow, 1883, a common haemoflagellate parasite of freshwater fish. The former taxon thus becomes a new junior synonym of T. carassii.
The reservoirs of dorso-abdominal scent glands and the occurrence of the metapleural scent gland evaporatoria in the adults of nine central European and one North American species in the family Rhopalidae (Hemiptera) were studied. All published data about the persistence of the dorso-abdominal scent glands in rhopalid adults are reviewed, and systematic and phylogenetic implications are derived from the patterns of variation.
The Galerucella nymphaeae species complex is a controversial group of leaf-consuming beetles with a Holarctic distribution. It includes several closely allied species or forms living in different habitats and utilizing different food plants. In northern Europe, two species are encountered, G. nymphaeae (L.) living on Nuphar, and G. sagittariae (Gyllenhal) living on semiaquatic or terrestrial plants, while all North American forms have been so far considered conspecific with the European G. nymphaeae. In the present study we have compared chorion polypeptides of the northern European G. nymphaeae and G. sagittariae with North American G. nymphaeae collected from Nuphar. The northern European G. nymphaeae was found to differ from both northern European G. sagittariae and North American G. nymphaeae, which were found to be virtually identical in respect to their chorion polypeptides. The present results, coupled with earlier data concerning e.g. egg morphology, structure of larval cuticle, and comparison of several life history traits, demonstrate that northern European G. nymphaeae and North American G. nymphaeae are not conspecific, and that the North American G. nymphaeae may be more closely allied to the northern European G. sagittariae.
Support Vector Machines (SVM) are well known as a kernel based method mostly applied to classification. SVM-Recursive Feature Elimination (SVM- RFE) is a variable ranking and selection method dedicated to the design of SVM based classifiers. In this paper, we propose to revisit the SVM-RFE method. We study two implementations of this feature selection method that we call External SVM-RFE and Internal SVM-RFE, respectively. The two implementations are applied to rank and select acoustic features extracted from speech to design optimized linear SVM classifiers that recognize speaker emotions. To show the efficiency of the External and Internal SVM-RFE methods, an extensive experimental study is presented. The SVM classifiers were selected using a validation procedure that ensures strict speaker independence. The results are discussed and compared with those achieved when the features are ranked using the Gram-Schmidt procedure. Overall, the results achieve a recognition rate that exceeds 90%.
In this paper, we generalize the noisy-or model. The generalizations are three-fold. First, we allow parents to be multivalued ordinal variables. Second, parents can have both positive and negative influences on their common child. Third, we describe how the suggested generalization can be extended to multivalued child variables. The major advantage of our generalizations is that they require only one parameter per parent. We suggest a model learning method and report results of experiments on the Reuters text classification data. The generalized noisy-or models achieve equal or better performance than the standard noisy-or. An important property of the noisy-or model and of its generalizations suggested in this paper is that it allows more efficient exact inference than logistic regression models do.
The GMDH MIA algorithm uses linear regression for adaptation. We show that Gauss-Markov conditions are not met here and thus estimations of network parameters are biased. To eliminate this we propose to use cloning of neuron parameters in the GMDH network with genetic selection and cloning (GMC GMDH) that can outperform other powerful methods. It is demonstrated on tasks from the Machine Learning Repository.
Ant-like stone beetles (Coleoptera: Scydmaenidae) include more than 4,850 described species in about 90 genera maintained as a separate cosmopolitan family since 1815. Recent authors have hypothesised that Scydmaenidae might be rooted deep inside rove-beetles (Staphylinidae). To test this hypothesis we analysed 206 parsimoniously informative larval and adult morphological characters scored for 38 taxa. Strict consensus topologies from the shortest trees in all 12 analyses consistently placed Scydmaenidae as sister to (Steninae + Euaesthetinae) in a monophyletic Staphylinine Group (with or without Oxyporinae). The single fully resolved and most consistently supported topology maintains a monophyletic Staphylinine Group consisting of Oxyporinae + (Megalopsidiinae + (("Scydmaenidae" + (Steninae + Euaesthetinae)) + (Leptotyphlinae + (Pseudopsinae + (Paederinae + Staphylininae))))); Solierius lacks larval data and is ambiguously placed within the Group. Eight analyses of variably aligned 18S rDNA data for 93 members of Staphylinoidea under parsimony, neighbour-joining and Bayesian approaches were markedly inconsistent, although partly congruent with the Scydmaenidae + (Steninae + Euaesthetinae) hypothesis. Our results strongly suggest that ant-like stone beetles do not form an independent family, but are morphologically modified members of Staphylinidae and, consequently, should be treated as a 32nd recent subfamily within the megadiverse Staphylinidae sensu latissimo. Formal taxonomic acts are: Scydmaeninae Leach, 1815, status novus (= Scydmaenidae Leach, 1815); Scydmaenitae Leach, 1815, status novus (= Scydmaeninae Leach, 1815); Mastigitae Fleming, 1821, status novus (= Mastiginae Fleming, 1821); Hapsomelitae Poinar & Brown, 2004, status novus (= Hapsomelinae Poinar & Brown, 2004). The family Staphylinidae sensu latissimo becomes the largest in Coleoptera and in the whole of the Animal Kingdom, with 55,440 described species (extant plus extinct), thus surpassing Curculionidae with an estimated 51,000 described species.