Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery

Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.

MPGN – An Approach for Discovering Class Association Rules

his article presents some of the results of the Ph.D. thesis Class Association Rule Mining Using MultiDimensional Numbered Information Spaces by Iliya Mitov (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt University, Faculty of Science on 15 November 2011 in Belgium

Classifier PGN: Classification with High Confidence Rules

ACM Computing Classification System (1998): H.2.8, H.3.3.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

DECISION TREE CLASSIFIERS FOR STAR/GALAXY SEPARATION

We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS-DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 <= r <= 21 (85.2%) and r >= 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT, and Ball et al. We find that our FT classifier is comparable to or better in completeness over the full magnitude range 15 <= r <= 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (> 80%) while simultaneously achieving low contamination (similar to 2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 <= r <= 21.

Associative priming in color naming: Interference and facilitation

In Experiment 1, color-naming interference for target stimuli following associated primes was greater in a group making a lexical decision to the prime than in a group reading the prime silently. High-frequency targets were responded to more quickly than low-frequency targets. In Experiment 2, with subjects naming the prime, there was evidence of associative interference when the prime and the target were grouped temporally but not when the intertrial interval was comparable with the prime-target interval. Associative primes presented at a short (120-msec) prime-target stimulus onset asynchrony facilitated color naming in Experiment 3. Taken together, the results suggest that the effect of faster processing of the base word in a color-naming task is facilitatory and that color-naming priming interference arises when associative prime processing increases conflict between word and color responses by enhancing phonological or articulatory activation of the base word.

Rewarded associative and instrumental conditioning after neonatal ventral hippocampus lesions in rats

Sprague Dawley rats were submitted to bilateral ventral hippocampus lesions 7 days after birth. This corresponds to the Lipska and Weinberger`s procedure for modeling schizophrenia. The aim of the present work was to test the learning capacity of such rats with an associative Pavlovian and an instrumental learning paradigm, both methods using reward outcome (food, sucrose or polycose). The associative paradigm comprised also a second learning test with reversed learning contingencies. The instrumental conditioning comprised an extinction test under outcome devaluation conditions. Neonatally lesioned rats, once adults (over 60 days of age), showed a conditioning deficit in the associative paradigm but not in the instrumental one. Lesioned rats remained able to adapt as readily as controls to the reversed learning contingency and were as sensitive as controls to the devaluation of outcome. Such observations indicate that the active access (instrumental learning) to a reward could have compensated for the deficit observed under the ""passive"" stimulus-reward associative learning condition. This feature is compared to the memory management impairments observed in clinical patients. (c) 2008 Elsevier B.V. All rights reserved.

Expectancy-based associative and identity priming in pronunciation

Evidence for expectancy-based priming in the pronunciation task was provided in three experiments. In Experiments 1 and 2, a high proportion of associatively related trials produced greater associative priming and superior retrieval of primes in a subsequent test of memory for primes, whereas high- and low-proportion groups showed comparable repetition benefits in perceptual identification of previously presented primes. In Experiment 2, the low-proportion condition had few associatively related pairs hut many identity pairs. In Experiment 3, identity priming was greater in a high- than a low-identity proportion group, with similar repetition benefits and prime retrieval responses for the two groups. These results indicate that when the prime-target relationship is salient, subjects strategically vary their processing of the prime according to the nature of the prime-target relationship.

DIFFERENT ROLE OF THE VENTRAL MEDIAL PREFRONTAL CORTEX ON MODULATION OF INNATE AND ASSOCIATIVE LEARNED FEAR

Reversible inactivation of the ventral portion of medial prefrontal cortex (vMPFC) of the rat brain has been shown to induce anxiolytic-like effects in animal models based on associative learning. The role of this brain region in situations involving innate fear, however, is still poorly understood, with several contradictory results in the literature. The objective of the present work was to verify in male Wistar rats the effects of vMPFC administration of cobalt chloride (CoCl(2)), a selective inhibitor of synaptic activity, in rats submitted to two models based on innate fear, the elevated plus-maze (EPM) and light-dark box (LOB), comparing the results with those obtained in two models involving associative learning, the contextual fear conditioning (CFC) and Vogel conflict (VCT) tests. The results showed that, whereas CoCl(2) induced anxiolytic-like effects in the CFC and VCT tests, it enhanced anxiety in rats submitted to the EPM and LOB. Together these results indicate that the vMPFC plays an important but complex role in the modulation of defensive-related behaviors, which seems to depend on the nature of the anxiety/fear inducing stimuli. (C) 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Ensemble classifiers in remote sensing: a comparative analysis

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Discriminative classifiers for speaker recognition

Speaker Recognition, Speaker Verification, Sparse Kernel Logistic Regression, Support Vector Machine

Machine learning with Lipschitz classifiers

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

Associative learning in wild Anastrepha obliqua females (Diptera, Tephritidae) related to a protein source

The aim of the present study was to determine whether wild adult Anastrepha obliqua (Macquart, 1835) females are able to associate a compound (quinine sulphate - QS) not related to their habitual diet with a protein-enriched food. Females were first fed on diets based on brewer yeast and sucrose containing or not QS. The groups were then allowed to choose between their original diets and a diet with or without QS, depending on the previous treatment, and between a diet based on agar and a diet containing agar and QS. When the nutritional value of the diets was adequate, the females did not show any preference for the diet with or without QS. With respect to the agar diet and the agar + QS diet, females previously fed on a nutritive diet containing QS preferred the diet containing QS, indicating an association between the compound and the nutritional value of the diet. The importance of this behavioral strategy is discussed.