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Fast Rule-Based Prediction of data Streams Using Associative Classification Mining

Fast Rule-Based Prediction of Data Streams Using Associative...

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international conference on IT Convergence and Security (ICITCS)

作者： K. Prasanna Lakshmi C. R. K. Reddy Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India

Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. this technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Increasing access to huge datasets and corresponding demands to analyze these data has led to the development of new online algorithms for performing machine learning on data streams. there is a need to develop a decision support system for predicting the huge datasets generated by various applications in IT industry. In this paper we proposed two efficient techniques called PSTMiner and PSToSWMine for prediction of data streams. this research uses associative classification which builds a classifier with prediction rules of high interestingness values. Experimental results show that the performance of these two algorithms is highly competitive in terms of accuracy and performance time.

关键词： Accuracy data models Prediction algorithms Algorithm design and analysis Classification algorithms Association rules

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Investigating Temporal and Spatial Trends of Brand Images using Twitter Opinion Mining

Investigating Temporal and Spatial Trends of Brand Images us...

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5th international conference on Information science and applications (ICISA)

作者： Cho, Seung Woo Cha, Moon Soo Kim, So Yeon Song, Joo Cheol Sohn, Kyung-Ah Ajou Univ Dept Informat & Comp Engn Suwon 441749 South Korea Ajou Univ Dept Digital Media Suwon 441749 South Korea

ISBN: (纸本)9781479944415

the growing popularity of social network services has led to many studies of various phenomena in this area. However, most of this research has been conducted using English language data, and relatively little has considered Korean. In this paper, we demonstrate a systematic analysis framework using Korean Twitter data to mine temporal and spatial trends of brand images. A publicly available Korean morpheme analyzer is used to analyze Korean tweets grammatically, and we construct Korean polarity dictionaries containing a noun, adjective, verb, and/or root to automatically analyze the sentiment of each tweet message. Sentiment classification is performed by a support vector machine and multinomial naive Bayes classifier. In particular, our own feature selection step improves the support vector machine sentiment classification accuracy to 80%. Based on this result, we visualize the temporal and spatial distribution of brand images, and present the temporal changes of brand-related keyword networks. Our analysis enables trends in brand awareness to be systematically traced and evaluated. this allows various other analyses, such as advantages and disadvantages of the brand, and a comparison with its competitors.

关键词： opinion mining sentiment classification social network service Twitter supervised learning natural language processing Korean twitter

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A sketch of a theory of visualization

A sketch of a theory of visualization

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5th international conference on Information Visualization theory and applications, IVAPP 2014

作者： Goebel, Randy Alberta Innovates Centre for Machine Learning Department of Computing Science University of Alberta Edmonton AB Canada

ISBN: (纸本)9789897580055

A picture results from a possibly multi-layer transformation of data to a visual vocabulary in which humans can draw inferences about the original data. the goal of this visualization process is to expose relationships amongst the data that are otherwise difficult to find, or only emerge by the process of the transformation. In case of the former kind of inference (confirming a relationship that did exist but was not obvious), visualization provides a kind of inferential amplifying effect. In the case of the latter (exposing new data relationships), visualization provides an inductive mechanism to create hypotheses not manifest in the original data. In this regard, the creation of pictures from data is about data compression, which is naturally a kind of machine learning. Just as statistical concepts like average and standard deviation provide a measure on properties of a set of numbers, so too does visualization provide a kind of "measure" on data compressed to a visual vocabulary presented as a picture. Our position is that visualization is about the (potentially multi-step, multilayered) transformation of data to pictures, and that ever such transformation must make choices about what kinds of relations to preserve, and what kinds of data artifacts to avoid in each such transformation. Like a chain of formal inference, conclusions following from the end result (the picture) are determined by what each transformation in the inference chain is intended to accomplish. We argue that the visualization of large data sets, too large to inspect directly, requires a rigorous theory of how to transform data to pictures, so that the scientists as observers can be assured that inferences drawn from the pictures are either confirmable in the detailed data, or at least plausible hypotheses which can be further pursued by seeking further data (evidence).

关键词： Visualization

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Mining classification rules from fuzzy min-max neural network 5

Mining classification rules from fuzzy min-max neural networ...

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5th international conference on Computing Communication and Networking Technologies, ICCCNT 2014

作者： Shinde, S.V. Kulkarni, U.V. Department Information Technology Pimpri Chinchwad College of Engineering Pune Maharastra India Department of Computer Science and Engineering SGGS Institute of Technology Nanded Maharastra India

ISBN: (纸本)9781479926961

the basic fuzzy min-max neural network (FMMN) is capable to perform the supervised classification of data. As like other artificial neural networks, FMMN is also like a black box and expressed in terms of min-max values and associated class label. So the justification of classification results given by FMMN is required to be obtained to make it more adaptive to the real world applications. this paper proposes the model to extract classification rules from trained FMMN. these rules justify the classification decision given by FMMN. For this FMMN is trained for the input data and resulting min-max values in the quantized form and class labels of created hyperboxes are passed as a input to the partial rule extraction (PART) algorithm of Waikato Environment for Knowledge Analysis (WEKA). As a result, more comprehensible rules expressed in terms human readable linguistic terms are extracted. the proposed model is applied to iris and wine dataset taken from the UCI machine learning repository. thus the extracted rules represent the trained FMMN in the more understandable form and can easily be applied to real world applications. © 2014 IEEE.

关键词： Membership functions

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Ridge body parts features for human pose estimation and recognition from RGB-D video data 5

Ridge body parts features for human pose estimation and reco...

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5th international conference on Computing Communication and Networking Technologies, ICCCNT 2014

作者： Jalal, Ahmad Kim, Yeonho Kim, Daijin Department of Computer Science and Engineering POSTECH Gyengbuk Korea Republic of

ISBN: (纸本)9781479926961

this paper addresses the issues of 3D human pose estimation, tracking and recognition from RGB-D video sequences using a generative structured framework. Most existing approaches focus on these issues using discriminative models. However, a discriminative model has certain drawbacks: a) it requires expensive training steps and large amount of training samples for covering inherently wide pose space, and (b) not suitable for real-time applications due to its slow algorithmic inferences. In this work, a real-time tracking system has been proposed for human pose recognition utilizing ridge body parts features. Initially, depth silhouettes extract ridge data inside the binary edges and initialize each body joints information using predefined pose. then, body parts tracking incorporates appearance learning to handle occlusions and manage body joints features. Lastly, Support Vector machine is used to recognize different poses. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. © 2014 IEEE.

关键词： Support vector machines

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A Sketch of a theory of Visualization

A Sketch of a Theory of Visualization

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conference on Information Visualization theory and applications

作者： Randy Goebel Alberta Innovates Centre for Machine Learning Department of Computing Science University of Alberta

ISBN: (纸本)9781479976874

关键词： Visualization Induction data Abstraction Picture Abstraction Dimensionality Reduction data Picture Mapping Domain Semantics

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RangeTree: A feature selection algorithm for C4.5 decision tree

RangeTree: A feature selection algorithm for C4.5 decision t...

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5th IEEE international conference on Intelligent Networking and Collaborative Systems, INCoS 2013

作者： Zhu, Hui Chen, Siyu Zhu, Lexiang Li, Hui Chen, Xiaofeng State Key Laboratory of Integrated Service Networks China Network and Data Security Key Laboratory of Sichuan Province Xidian University Xi'an China

ISBN: (纸本)9780769549880

In order to conduct fine-grained network management in mobile network, Traffic Classification or Detection is widely used to divide network traffic into different classes, according to their source applications. Many techniques are exploited in Traffic Classification. Among them, machine learning has grown considerably attention because of its accuracy. Feature selection chooses feature combinations for machine learning algorithms, and has significant influence on the accuracy and efficiency. To discover optimal features, all possible combinations need to be evaluated by testing real classifiers. With numerous features, feature selection can cost an abundance of time and computational resources. this paper proposes a feature selection algorithm for C4.5 Decision Tree. this algorithm utilizes structural characteristics of C4.5 algorithm to exclude some of the combinations without actually testing the classifiers. the simulation results demonstrate that the algorithm can reduce the number of tests in seeking the optimal feature combination. © 2013 IEEE.

关键词： Feature Selection

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An Ensemble Incremental Approach of Extreme learning machine (ELM) For Paddy Growth Stages Classification Using MODIS Remote Sensing Images

An Ensemble Incremental Approach of Extreme Learning Machine...

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5th international conference on Advanced Computer science and Information Systems (ICACSIS)

作者： Mulyono, S. Pianto, T. A. Fanany, M. Ivan Basaruddin, T. Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9789791421195

Remote sensing technology plays an important role in agriculture applications, especially for paddy growth stages classification, which is a critical process in predicting crop production. the analysis of multi bands data covering very large swath areas using iterative methods such as neural network or SVM will certainly cost much computation time. this paper addresses this problem by taking advantage from a non-iterative tuning capability of Extreme learning machine (ELM) for paddy growth stages classification using MODIS (Moderate Resolution Imaging Spectroradiometer) remote sensing images. the accuracy of classification is measured by Cohen's kappa. Seven classes are used in classification, with consist of six classes for paddy growth stages and one class for dominated cloud. the contribution of this study is a new ensemble incremental approaches based on random bootstrap resampling for basic ELM (B-ELM) and Error Minimized ELM (EM-ELM) are applied to build multi-class classifier using two types of hidden nodes function, i.e. additive and radial basis function (RBF) hidden nodes. the classification results were compared each other with these two types of hidden nodes. Our ensemble incremental approach successfully classify seven paddy growth stages and significantly improve the overall kappa coefficient to 10.2% higher with only in average 7 nodes addition overhead.

关键词： Elm Ecosystem models Developmental stages Growth stage Moderate Resolution Imaging Spectroradiometer Rice remote sensing images learning machines Remote Sensing Technologies Taxonomy Incremental

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Application of support vector machines to the classification of galaxy morphologies

Application of support vector machines to the classification...

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2013 5th international conference on Computational and Information sciences, ICCIS 2013

作者： Freed, Matthew Lee, Jeonghwa Department of Computer Science Shippensburg University Shippensburg PA 17257 United States

ISBN: (纸本)9780769550046

Classifying galaxies into categories based on their structure has many practical applications in astronomy. In particular, large catalogues of classified galaxy images have been useful in many studies of the universe. However, one of the premier data sources in astronomy, the Sloan Digital Sky Survey (SDSS), does not provide classification information for the 50 million galaxy images it contains. As there are simply too many objects to classify manually, machine learning and classification algorithms are required to automate this process. this research applies the Support Vector machine (SVM) algorithm to classify galaxy morphologies. the accuracy of the classification is measured on various categories of galaxies from the survey. A three class algorithm is presented that makes use of multiple SVMs. this algorithm is used to assign the categories of spiral, elliptical, and irregular galaxies. © 2013 IEEE.

关键词： data mining

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Using Iterative MapReduce for Parallel Virtual Screening

Using Iterative MapReduce for Parallel Virtual Screening

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5th IEEE international conference on Cloud Computing Technology and science (IEEE CloudCom)

作者： Ahmed, Laeeq Edlund, Ake Laure, Erwin Spjuth, Ola Royal Inst Technol Dept HPCViz Stockholm Sweden Uppsala Univ Dept Pharmaceut Biosci Uppsala Sweden

ISBN: (纸本)9781479915484

Virtual Screening is a technique in chemoinformatics used for Drug discovery by searching large libraries of molecule structures. Virtual Screening often uses SVM, a supervised machine learning technique used for regression and classification analysis. Virtual screening using SVM not only involves huge datasets, but it is also compute expensive with a complexity that can grow at least up to O(n(2)). SVM based applications most commonly use MPI, which becomes complex and impractical with large datasets. As an alternative to MPI, MapReduce, and its different implementations, have been successfully used on commodity clusters for analysis of data for problems with very large datasets. Due to the large libraries of molecule structures in virtual screening, it becomes a good candidate for MapReduce. In this paper we present a MapReduce implementation of SVM based virtual screening, using Spark, an iterative MapReduce programming model. We show that our implementation has a good scaling behaviour and opens up the possibility of using huge public cloud infrastructures efficiently for virtual screening.

关键词： MapReduce Big data Spark Chemoinformatics Parallel SVM

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