Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold

[[abstract]]Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120610~20120615[[iscallforpapers]]Y[[conferencelocation]]Brisbane, Australi

Map-reduced based approach for mining group stock portfolio

[[abstract]]In this paper, the map-reduce technique is utilized for speeding up the mining process and derived as similar results as our previous approach. The chromosome representation consists of four parts that are a mapper number, grouping part, stock part and portfolio part. According to mapper number, chromosomes in population are divided into subsets and sent to respective mappers. Fitness evaluation and genetic operations are the same with our previous approach, and executed on reducers. The evolution process is repeated until reaching the terminal conditions. Experiments are conducted on a real dataset to show the performance of proposed approach.[[notice]]補正完

An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach

Adhesive L1CAM-Robo Signaling Aligns Growth Cone F-Actin Dynamics to Promote Axon-Dendrite Fasciculation in C. elegans

Neurite fasciculation through contact-dependent signaling is important for the wiring and function of the neuronal circuits. Here, we describe a type of axon-dendrite fasciculation in C. elegans, where proximal dendrites of the nociceptor PVD adhere to the axon of the ALA interneuron. This axon-dendrite fasciculation is mediated by a previously uncharacterized adhesive signaling by the ALA membrane signal SAX-7/L1CAM and the PVD receptor SAX-3/Robo but independent of Slit. L1CAM physically interacts with Robo and instructs dendrite adhesion in a Robo-dependent manner. Fasciculation mediated by L1CAM-Robo signaling aligns F-actin dynamics in the dendrite growth cone and facilitates dynamic growth cone behaviors for efficient dendrite guidance. Disruption of PVD dendrite fasciculation impairs nociceptive mechanosensation and rhythmicity in body curvature, suggesting that dendrite fasciculation governs the functions of mechanosensory circuits. Our work elucidates the molecular mechanisms by which adhesive axon-dendrite signaling shapes the construction and function of sensory neuronal circuits

Evolution of Interferon-Based Therapy for Chronic Hepatitis C

Since 1986, interferon-alfa (IFN-α) monotherapy has been administered for patients with chronic hepatitis C (CHC). However, sustained response rate is only about 8% to 9%. Subsequent introduction of ribavirin in combination with IFN-α was a major breakthrough in the treatment of CHC. Sustained virological responses (SVRs) rate is about 30% in hepatitis C virus genotype 1 (HCV-1) patients, and is about 65% in HCV-2 or -3 patients. After 2000, pegylated interferon (PegIFN) much improved the rates of SVR. Presently, PegIFN-α-ribavirin combination therapy has been current standard of care for patients infected with HCV. In patients with HCV-1, treatment for 48 weeks is optimal, but 24 weeks of treatment is sufficient in HCV-2 or -3 infected patients. Clinical factors have been identified as predictors for the efficacy of the IFN-based therapy. The baseline factor most strongly predictive of an SVR is the presence of HCV-2 or -3 infections. Rapid virological response (RVR) is the single best predictor of an SVR to PegIFN-ribavirin therapy. If patients can't achieve a RVR but achieve a complete early virological response (cEVR), treatment with current standard of care can provide more than 90% SVR rate. HCV-1 patients who do not achieve an EVR should discontinue the therapy. Recent advances of protease inhibitor may contribute the development of a novel triple combination therapy