Optimising ECOC matrices in multi-class classification problems

Error Correcting Output Coding (ECOC) is a multi-class classiffication technique in which multiple binary classiffiers are trained according to a preset code matrix, such that each one learns a separate dichotomy of the classes. While ECOC is one of the best solutions to multi-class problems, it is suboptimal since the code matrix and the base classiffiers are not learned simultaneously. In this thesis, we present three different algorithms that iteratively updates the ECOC code matrix to improve the performance of the ensemble by reducing the decoupling. Firstly, we applied the previously developed FlipECOC+ update algorithm. Second method is applying simulated annealing method on updating ECOC matrix by flipping proposed entries according to ascending order. Last method is applying beam search to find updated ECOC matrix which has highest validation accuracy. We applied all three algorithms on UCI (University of California Irvine) data sets. Beam search algorithm gives the best result on UCI data sets. All of the proposed update algorithms does not involve further training of the classiffiers and can be applied to any ECOC ensemble

Image-based Text Classification using 2D Convolutional Neural Networks

We propose a new approach to text classification in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations of the visual patterns of words. Our approach demonstrates that it is possible to get semantically meaningful features from images with text without using optical character recognition and sequential processing pipelines, techniques that traditional natural language processing algorithms require. To validate our approach, we present results for two applications: text classification and dialog modeling. Using a 2D Convolutional Neural Network, we were able to outperform the state-ofart accuracy results for a Chinese text classification task and achieved promising results for seven English text classification tasks. Furthermore, our approach outperformed the memory networks without match types when using out of vocabulary entities from Task 4 of the bAbI dialog dataset

Fluorimetric detection of boron by azomethine-H in micellar solution and sol-gel

Mixtures of boron and azomethine-H in solution result in slow complexation. Addition of sodium dodecyl sulfate (SDS), polyethylene glycol dodecyl ether (Brij-35), 4-(1,1,3,3-tetramethylbutyl)phenyl-polyethylene glycol (TritonX-100), and cetyltrimethyl ammonium bromide (CTAB) result in considerable decrease in complexation time and enhancement in signal of peak in solution and also sol-gel. The fluorescence of the complex is monitored at an emission wavelength of 486 nm with excitation at 416 nm. The presence of 1 x 10(-3) mol L-1 SDS decreased the complexation time up to 10 min in solution and 20 min in sol-gel for above 0.25 mu g B mL(-1) and 30 min in solution and 35 min in sol-gel for below 0.25 mu g B mL(-1). However, the photostability did not change by adding micelle in both media. The proposed method shows a linear response toward boron in the concentration range of 0.05-10 mu g mL(-1) and is selective for boron over a large number of electrolytes and cations. The detection limit was 7 mu g L-1. This method has been used for the detection of boron in environmental water samples and fruit juices with satisfactory results