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Parameters Optimization for Nonparallel Support Vector machine by Particle Swarm Optimization

Parameters Optimization for Nonparallel Support Vector Machi...

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4th international conference on Information Technology and Quantitative Management (ITQM) - Promoting Business Analytics and Quantitative Management of Technology

作者： Bamakan, Seyed Mojtaba Hosseini Wang, Huadong Ravasan, Ahad Zare Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Allameh Tabatabai Univ Dept Ind Management Tehran Iran

Support vector machine is a well-known and computationally powerful machine learning technique for pattern classification and regression problems, which has been successfully applied to solve many practical problems in a wide variety of fields. Nonparallel Support Vector machine (NPSVM) which is an extension of Twin-SVMs, is proved to be theoretically and practically more flexible and superior than TWSVMs and also it overcomes several drawbacks of the existing typical SVMs in order to be applicable in large-scale data sets. However, one of the difficulties in successful implementation of NPSVM is its different parameters, which should be well adjusted during the training process. In fact, the generalization power, robustness and sparsity of NPSVM are extremely depended on well setting of its parameters. In this paper, we propose a hybrid approach for parameter determination of the NPSVM by Particle Swarm Optimization techniques. Furthermore, in order to increase the sparsity of NPSVM and to reduce the training time, we take into account the number of support vectors (SVs) along with classification accuracy as a weighted objective function. Our experiments on several public datasets show that the proposed method can achieve better classification accuracy compare to that of TWSVM and NPSVM with less computational time. (C) 2016 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license.

关键词： Nonparallel Support Vector machine Particle Swarm Optimization Twin SVM Parameter setting

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D2PCP: DISSECTING DISTRIBUTED data TO PREDICT CRIME pattern USING HADOOP PLATFORM 4

D2PCP: DISSECTING DISTRIBUTED DATA TO PREDICT CRIME PATTERN ...

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4th IEEE international conference on Parallel, Distributed and Grid Computing (IEEE PDGC)

作者： Partap, Manu Chopra, Anshu Sri Sai Coll Engn & Technol Dept Comp Sci & Engn Badhani Punjab India

ISBN: (纸本)9781509036691

Basic numerical synopses normal, least, whole were adequate for the business issues of the 1980s and 1990s. A lot of complex information, however, requires new systems. Perceiving client inclinations requires investigation of procurement history, yet, likewise a nearby examination of searching conduct and items saw, remarks furthermore, audits signed on a site, and even grumblings and issues raised with client bolster staff. Foreseeing conduct requests that clients be assembled by their inclinations, so that conduct of one individual in the gathering can be utilized to anticipate the conduct of others. the calculations included incorporate characteristic dialect handling, example acknowledgment, machine learning and that's only the tip of the iceberg. these methods run extremely well on Hadoop. In this research we are propose PCP Predict crime pattern approach in hadoop for detect the pattern from the distributed warehouse.

关键词： Hadoop data DISSECTING Distributed Computing Cloud Computing data Mining

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A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association 12th

A New Strategy for Case-Based Reasoning Retrieval Using Clas...

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12th international conference on machine learning and data Mining (MLDM)

作者： Aljuboori, Ahmed Meziane, Farid Parsons, David Univ Salford Sch Comp Sci & Engn Salford M5 4WT Lancs England

ISBN: (纸本)9783319419206;9783319419190

this paper proposes a novel strategy, Case-Based Reasoning Using Association Rules (CBRAR) to improve the performance of the Similarity base Retrieval SBR, classed frequent pattern trees FP-CAR algorithm, in order to disambiguate wrongly retrieved cases in Case-Based Reasoning (CBR). CBRAR use class association rules (CARs) to generate an optimum FP-tree which holds a value of each node. the possible advantage offered is that more efficient results can be gained when SBR returns uncertain answers. We compare the CBR Query as a pattern with FP-CAR patterns to identify the longest length of the voted class. If the patterns are matched, the proposed strategy can select not just the most similar case but the correct one. Our experimental evaluation on real data from the UCI repository indicates that the proposed CBRAR is a better approach when compared to the accuracy of the CBR systems used in our experiments.

关键词： Class Association Rules Frequent pattern trees Case-Based Reasoning Retrieval P-trees

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Semantic Aware Bayesian Network Model for Actionable Knowledge Discovery in Linked data 1

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12th international conference on machine learning and data Mining (MLDM)

作者： Alharbi, Hasanein Saraee, Mohamad Univ Salford Salford M5 4WT Lancs England

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

the majority of the conventional mining algorithms treat the mining process as an isolated data-driven procedure and overlook the semantic of the targeted data. As a result, the generated patterns are abundant and end users cannot act upon them seamlessly. Furthermore, interdisciplinary knowledge can not be obtained from domain-specific silo of data. the emergence of Linked data (LD) as a new model for knowledge representation, which intertwines data with its semantics, has introduced new opportunities for data miners. Accordingly, this paper proposes an ontology-based Semantic-Aware Bayesian network (BN) model. In contraxt to the exisiting mining algorithms, the proposed model does nto transorm the original format of the LD set. therefore, it not only accomodates the sematnic aspects in LD, but also caters to the need of connectign different data-sets from different domains. We evaluate the proposed model on a Bone Dysplasia dataset, Experimental results show promising perfomance.

关键词： Linked data (LD) Actionable Knowledge Discovery (AKD) Bayesian Network (BN)

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Architecture Design of pattern Detection System for Smart Cities datasets 12

Architecture Design of Pattern Detection System for Smart Ci...

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12th IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Bojan, Valentina-Camelia Raducu, Ionut-Gabriel Pop, Florin Mocanu, Mariana Cristea, Valentin Univ Politehn Bucuresti Dept Comp Sci Fac Automat Control & Comp Bucharest Romania

ISBN: (纸本)9781509038985

Nowadays, there is more and more interest in the research and development of systems, applications, tools or frameworks for 'smart' environments. We do not want to bother with useless actions or decisions anymore because we want to spent our time doing more valuable activities. this would only be one reason to put the bases and after that to build a platform able to extract patterns and useful information from data measured by devices that monitor the 'smart' environment. A platform of this kind would become the main reason for the environment to be a 'smart' one and for more people to understand the value of such an environment. In this paper we investigate the need of a global and generic platform able to work with many type of datasets, with various systems and to serve different 'Smart cities' applications. through this platform we aim to unify the need of all 'Smart cities' systems for having and using mined data, patterns extracted from the generated (measured) raw data.

关键词： data analysis distributed processing machine learning pattern recognition time series smart cities

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Clinical Named Entity recognition: Challenges and opportunities 4

Clinical Named Entity Recognition: Challenges and opportunit...

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4th IEEE international conference on Big data (Big data)

作者： Kundeti, Srinivasa Rao Vijayananda, J. Mujjiga, Srikanth Kalyan, M. Philips Healthcare Data Sci Bangalore Karnataka India

ISBN: (纸本)9781467390057

Information Extraction (IE), one of the important tasks in text analysis and Natural Language Processing (NLP), involves extracting meaningful pieces of knowledge from unstructured information sources, as unstructured data is computationally opaque. the intent of IE is to produce a knowledge base i.e. organize the information in a way that it is useful to people and arrange the information in a semantic way so that algorithms can make certain useful inferences from it. Named Entity recognition (NER) is a sub-task of IE which finds and classifies the names/entities. Once these Named Entities (NE) are extracted, they can then be indexed and made searchable, relations can be derived, questions can be answered and many more. NER techniques are different for different domains, because of the uniqueness that exists in each of the domains, although the process depends on a number of fundamental Natural Language Processing (NLP) steps such as tokenization, part-of-speech tagging, parsing and model building. As an example, NER in the medical domain involves handling of a number of vital tasks such as identification of medical terms, attributes such as negation, severity, identification of relationships between entities and mapping terms in the document to concepts in domain specific ontologies. there is also a heavy dependence on domain specific resources such as medical dictionaries and ontologies such as the Unified Medical Language System (UMLS)[34]. In this paper, we focus on NER in the clinical domain. In particular, we will focus on the NER challenges and the qualitative analysis of clinical reports on the approaches we took for the named entities: anatomies, findings, location qualifier, and procedures.

关键词： Natural Language Processing machine learning Text Analytics Deep learning Ontologies Language Modelling Long short-term memory

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Giving voice to office customers: Best practices in how office handles verbatim text feedback 4

Giving voice to office customers: Best practices in how offi...

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4th IEEE international conference on Big data, Big data 2016

作者： Bentley, Michael Batra, Soumya Office Product Group Microsoft RedmondWA United States

ISBN: (纸本)9781467390040

Microsoft Office users submit hundreds of thousands of pieces of verbatim feedback per month. How can an engineer or manager in Office find the signal in this data to make business decisions? this paper presents an overview of the Office Customer Voice (OCV) system. OCV combines classification, on-demand clustering and other machine learning techniques with a rich web UI to solve this problem. In this paper, we describe the different types of feedback received. Next, we outline the architecture used to build OCV. We then detail the text processing, classification and clustering done to reason on the data. Finally, we present challenges, future plans, and best practices that may be relevant to other teams analyzing customer feedback. We argue that this multi-pronged approach to handling customer feedback presents a pattern that other organizations can use to mature their handling of customer feedback. © 2016 IEEE.

关键词： learning systems

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Hyperalignment of Multi-subject fMRI data by Synchronized Projections 4th

Hyperalignment of Multi-subject fMRI Data by Synchronized Pr...

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4th international Workshop on machine learning and Interpretation in Neuroimaging (MLINI) Held at conference on Neural Information Processing Systems (NIPS)

作者： Rustamov, Raif M. Guibas, Leonidas Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9783319451732;9783319451749

Group analysis of fMRI data via multivariate pattern methods requires accurate alignments between neuronal activities of different subjects in order to attain competitive inter-subject classification rates. Hyperalignment, a recent technique pioneered by Haxby and collaborators, aligns the activations of different subjects by mapping them into a common abstract high-dimensional space. While hyperalignment is very successful in terms of classification performance, its "anatomy free" nature excludes the use of potentially helpful information inherent in anatomy. In this paper, we present a novel approach to hyperalignment that allows incorporating anatomical information in a non-trivial way. Experiments demonstrate the effectiveness of our approach over the original hyperalignment and several other natural alternatives.

关键词： Classification (of information)

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Deep learning in the Automotive Industry: Applications and Tools 4

Deep Learning in the Automotive Industry: Applications and T...

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4th IEEE international conference on Big data (Big data)

作者： Luckow, Andre Cook, Matthew Ashcraftt, Nathan Weill, Edwin Djerekarov, Emil Vorster, Bennie BMW Grp IT Res Ctr Informat Management Amer Greenville SC 29607 USA Clemson Univ Clemson SC USA Univ Cincinnati Cincinnati OH USA

ISBN: (纸本)9781467390057

Deep learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-theart in libraries, tools and infrastructures (e.g. GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.

关键词： Deep learning Cloud Computing Automotive Manufacturing

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Reverse Engineering Smart card Malware using Side Channel Analysis with machine learning Techniques 4

Reverse Engineering Smart card Malware using Side Channel An...

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4th IEEE international conference on Big data (Big data)

作者： Tsague, Hippolyte Djonon Twala, Bheki CSIR MDS Smart Token Res Grp Pretoria South Africa Univ Johannesburg Dept Elect & Elect Engn Sci Inst Intelligent Syst Fac Engn Johannesburg South Africa

ISBN: (纸本)9781467390057

From inception, side channel leakage has been widely used for the purposes of extracting secret information, such as cryptographic keys, from embedded devices. However, in a few instances it has been utilized for extracting other information about the internal state of a computing device. In this paper, we exploit side channel information to recover large parts of the Sykipot malware program executed on a smart card. We present the first methodology to recover the program code of a smart card malware by evaluating its power consumption only. Besides well-studied methods from side channel analysis, we apply a combination of dimensionality reduction techniques in the form of PCA and LDA models to compress the large amount of data generated while preserving as much variance of the original data as possible. Among feature extraction techniques, PCA and LDA are very common dimensionality reduction algorithms that have successfully been applied in many classification problems like face recognition, character recognition, speech recognition, etc. with the chief objective being to eliminate insignificant data (without losing too much information) during the pre-processing step. In addition to quantifying the potential of the created side channel based disassembler, we highlight its diverse and unique application scenarios.

关键词： Side Channel Leakage Electromagnetic Templates Principal Components Analysis Linear Discriminant Analysis Multivariate Gaussian Distribution k-Nearest Neighbours Algorithm Reverse Engineering

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