Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.

Differential expression of Egr-1-like DNA-binding activities in the naive rat brain and after excitatory stimulation

Egr-1 and related proteins are inducible transcription factors within the brain recognizing the same consensus DNA sequence. Three Egr DNA-binding activities were observed in regions of the naive rat brain. Egr-1 was present in all brain regions examined. Bands composed, at least in part, of Egr-2 and Egr-3 were present in different relative amounts in the cerebral cortex, striatum, hippocampus, thalamus, and midbrain. All had similar affinity and specificity for the Egr consensus DNA recognition sequence. Administration of the convulsants NMDA, kainate, and pentylenetetrazole differentially induced Egr-1 and Egr-2/3 DNA-binding activities in the cerebral cortex, hippocampus, and cerebellum. All convulsants induced Egr-1 and Egr-2 immunoreactivity in the cerebral cortex and hippocampus. These data indicate that the members of the Egr family are regulated at different levels and may interact at promoters containing the Egr consensus sequence to fine tune a program of gene expression resulting from excitatory stimuli.

Indefinite determiner introducing definite referent: a special use of 'yi 'one' + classifier' in Chinese

The indefinite determiner yi 'one'+ classifier' is the most approximate to an indefinite article, like the English a, in Chinese. It serves all the functions characteristic of representative stages of grammaticalization from a numeral to a generalized indefinite determiner as elaborated in the literature. It is established in this paper that the Chinese indefinite determiner has developed a special use with definite expressions, serving as a backgrounding device marking entities as of low thematic importance and unlikely to receive subsequent mentions in ensuing discourse. 'yi+ classifier' in the special use with definite expressions displays striking similarities in terms of semantic bleaching and phonological reduction with the same determiner at the advanced stage of grammaticalization characterized by uses with generics, nonspecifics and nonreferentials. An explanation is offered in terms of an implicational relation between nonreferentiality and low thematic importance which characterize the two uses of the indefinite determiner. While providing another piece of evidence in support of the claim that semantically nonreferentials and entities of low thematic importance tend to be encoded in terms of same linguistic devices in language, findings in this paper have shown how an indefinite determiner can undergo a higher degree of grammaticalization than has been reported in the literature-it expands its scope to mark not only indefinite but also definite expressions as semantically nonreferential and/or thematically unimportant. (C) 2003 Elsevier B.V. All rights reserved.

Gene-expression profiling reveals distinct expression patterns for classic versus variant Merkel cell phenotypes and new classifier genes to distinguish Merkel cell from small-cell lung carcinoma

Merkel cell carcinoma (MCC) is a rare aggressive skin tumor which shares histopathological and genetic features with small-cell lung carcinoma (SCLC), both are of neuroendocrine origin. Comparable to SCLC, MCC cell lines are classified into two different biochemical subgroups designated as 'Classic' and 'Variant'. With the aim to identify typical gene-expression signatures associated with these phenotypically different MCC cell lines subgroups and to search for differentially expressed genes between MCC and SCLC, we used cDNA arrays to pro. le 10 MCC cell lines and four SCLC cell lines. Using significance analysis of microarrays, we defined a set of 76 differentially expressed genes that allowed unequivocal identification of Classic and Variant MCC subgroups. We assume that the differential expression levels of some of these genes reflect, analogous to SCLC, the different biological and clinical properties of Classic and Variant MCC phenotypes. Therefore, they may serve as useful prognostic markers and potential targets for the development of new therapeutic interventions specific for each subgroup. Moreover, our analysis identified 17 powerful classifier genes capable of discriminating MCC from SCLC. Real-time quantitative RT-PCR analysis of these genes on 26 additional MCC and SCLC samples confirmed their diagnostic classification potential, opening opportunities for new investigations into these aggressive cancers.

Behavioral and physiological responses to stabling in naive horses

The purpose of this study was to investigate the response of horses to confinement and isolation in a stable (indoor individual housing) for the first time using behavioral indices, heart rate, and salivary cortisol concentration. Six naive 2-year-old Australian Stock Horse fillies were examined at 4-hour intervals over 24 hours in an outdoor group paddock followed by 24 hours in indoor individual housing. Behavioral observations and scores and heart rates were recorded and saliva samples were taken at each interval. During stabling, all horses became agitated and demonstrated increased vocalization and movement. Behavioral scores were significantly higher in the indoor individual housing (P

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

Acquisition of the English article system in SLA and the Missing Surface Inflection Hypothesis

This paper examines the article system in interlanguage grammar focusing on Japanese learners of English, whose native language lacks articles. It will be demonstrated that for the acquisition of the English article system, count/mass distinctions and definiteness are the crucial factors. Although Japanese does not employ the article system to encode these aspects, it will be argued that they are nevertheless syntactically encoded through its classifier system. Hence, the problem for these learners must be to map these features onto the appropriate surface forms as the Missing Surface Inflection Hypothesis predicts (Prévost & White 2000). This suggestion will further be supported empirically by a fill-in-the article task. It will be concluded that these Japanese learners understand the English article system fairly well, possibly due to their native language, yet have problems with realizing the relevant features (i.e. count/mass distinctions and definiteness) in the target language.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Chronic ethanol exposure and withdrawal influence NMDA receptor subunit and splice variant mRNA expression in the rat cerebral cortex

Chronic ethanol exposure and subsequent withdrawal are known to change NMDA receptor activity. This study examined the effects of chronic ethanol administration and withdrawal on the expression of several NMDA receptor subunit and splice variant mRNAs in the rat cerebral cortex. Ethanol dependence was induced by ethanol vapour exposure. To delineate between seizure-induced changes in expression during withdrawal and those due to withdrawal per se, another group of naive rats was treated with pentylenetetrazol (PTZ) injection (30 mg/kg, i.p.). RNA samples from the cortices of chronically treated and withdrawing animals were compared to those from pairfed controls. Changes in NMDA receptor mRNA expression were determined using ribonuclease protection assays targetting the NR2A, -2B, -2C and NR1-pan subunits as well as the three alternatively spliced NR1 inserts (NR1-pan describes all the known NR1 splice variants generated from the 5' insert and the two 3' inserts). The ratio of NR1 mRNA incorporating the 5' insert vs, that lacking it was decreased during ethanol exposure and up to 48 h after withdrawal. NR2B mRNA expression was elevated during exposure, but returned to control levels 18 h after withdrawal. Levels of NR2A, NR2C, NR1-pan and both 3' NR1 insert mRNAs from the ethanol-treated groups did not alter compared with the pair-fed control group. No changes in the level of any NMDA receptor subunit mRNA was detected in the PTZ-treated animals. These data support the hypothesis that changes in NMDA receptor subunit composition may underlie a neuronal adaptation to the chronic ethanol-inhibition and may therefore be important in the precipitation of withdrawal hyperactivity. (C) 1999 Elsevier Science B.V. All rights reserved.

Incomplete, asymmetric, and route-dependent cross-tolerance between oxycodone and morphine in the Dark Agouti rat

Our previous studies indicate that oxycodone is a putative kappa-opioid agonist, whereas morphine is a well documented mu-opioid agonist. Because there is limited information regarding the development of tolerance to oxycodone, this study was designed to 1) document the development of tolerance to the antinociceptive effects of chronically infused i.v. oxycodone relative to that for i.v. morphine and 2) quantify the degree of antinociceptive cross-tolerance between morphine and oxycodone in adult male Dark Agouti (DA) rats. Antinociceptive testing was performed using the tail-flick latency test. Complete antinociceptive tolerance was achieved in 48 to 84 h after chronic infusion of equi-antinociceptive doses of i.v. oxycodone (2.5 mg/24 h and 5 mg/24 h) and i.v. morphine (10 mg/24 h and 20 mg/24 h, respectively). Dose-response curves for bolus doses of i.v. and i.c.v. morphine and oxycodone were produced in naive, morphine-tolerant, and oxycodone-tolerant rats. Consistent with our previous findings that oxycodone and morphine produce their intrinsic antinociceptive effects through distinctly different opioid receptor populations, there was no discernible cross-tolerance when i.c.v. oxycodone was given to morphine-tolerant rats. Similarly, only a low degree of cross-tolerance (approximate to 24%) was observed after i.v. oxycodone administration to morphine-tolerant rats. By contrast, both i.v. and i.c.v. morphine showed a high degree of cross-tolerance (approximate to 71% and approximate to 54%, respectively) in rats rendered tolerant to oxycodone. Taken together, these findings suggest that, after parenteral but not supraspinal administration, oxycodone is metabolized to a mu-opioid agonist metabolite, thereby explaining asymmetric and incomplete cross-tolerance between oxycodone and morphine.

A landscape-scale test of the predictive ability of a spatially explicit model for population viability analysis

1. Although population viability analysis (PVA) is widely employed, forecasts from PVA models are rarely tested. This study in a fragmented forest in southern Australia contrasted field data on patch occupancy and abundance for the arboreal marsupial greater glider Petauroides volans with predictions from a generic spatially explicit PVA model. This work represents one of the first landscape-scale tests of its type. 2. Initially we contrasted field data from a set of eucalypt forest patches totalling 437 ha with a naive null model in which forecasts of patch occupancy were made, assuming no fragmentation effects and based simply on remnant area and measured densities derived from nearby unfragmented forest. The naive null model predicted an average total of approximately 170 greater gliders, considerably greater than the true count (n = 81). 3. Congruence was examined between field data and predictions from PVA under several metapopulation modelling scenarios. The metapopulation models performed better than the naive null model. Logistic regression showed highly significant positive relationships between predicted and actual patch occupancy for the four scenarios (P = 0.001-0.006). When the model-derived probability of patch occupancy was high (0.50-0.75, 0.75-1.00), there was greater congruence between actual patch occupancy and the predicted probability of occupancy. 4. For many patches, probability distribution functions indicated that model predictions for animal abundance in a given patch were not outside those expected by chance. However, for some patches the model either substantially over-predicted or under-predicted actual abundance. Some important processes, such as inter-patch dispersal, that influence the distribution and abundance of the greater glider may not have been adequately modelled. 5. Additional landscape-scale tests of PVA models, on a wider range of species, are required to assess further predictions made using these tools. This will help determine those taxa for which predictions are and are not accurate and give insights for improving models for applied conservation management.