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Proceedings of the 3rd international conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014 Volume 1

引用

2014年

作者： Suresh Chandra Satapathy Bhabendra Narayan Biswal Siba Kumar Udgata Jyotsna Kumar Mandal

ISBN: (纸本)9783319119328

This volume contains 95 papers presented at FICTA 2014: Third international conference on Frontiers in Intelligent Computing: Theory and Applications. The conference was held during 14-15, November, 2014 at Bhubaneswar, Odisha, India. This volume contains papers mainly focused on data Warehousing and mining, machine learning, Mobile and Ubiquitous Computing, AI, E-commerce & Distributed Computing and Soft Computing, Evolutionary Computing, Bio-inspired Computing and its Applications.

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The Nearest Centroid Based on Vector Norms: A New Classification Algorithm for a New Document Representation Model

The Nearest Centroid Based on Vector Norms: A New Classifica...

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10th international conference on machine learning and data mining (MLDM)

作者： Mountassir, Asmaa Benbrahim, Houda Berrada, Ilham Mohamed 5 Univ ENSIAS ALBIRONI Res Team Rabat Morocco

ISBN: (纸本)9783319089799;9783319089782

In this paper, we present a novel model that we propose for document representation. In contrast with the classical Vector Space Model which represents each document by a unique vector in the feature space, our model consists in representing each document by a vector in the space of training documents of each category. We develop, for this novel model, a discriminative classifier which is based on the norms of the generated vectors by our model. We call this algorithm the Nearest Cetroid based on Vector Norms. Our major goal, by the proposition of such new classification framework, is to overcome the problems related to huge dimensionality and vector sparsity which are commonly faced in Text Classification problems. We evaluate the performance of the proposed framework by comparing its effectiveness and efficiency with those of some standard classifiers when used with the classical document representation. The studied classifiers are Naive Bayes (NB), Support Vector machines (SVM) and k-Nearest Neighbors (kNN). We conduct our experiments on multi-lingual balanced and unbalanced binary data sets. Our results show that our algorithm typically performs well since it is competitive with the classical methods and, at the same time, dramatically faster especially in comparison with NB and kNN. We also apply our model on the Reuters21578 corpus so as to evaluate its performance in a multi-class environment. We can say that the obtained result (85.4% in terms of micro-F1) is promising and that it can be improved in future works.

关键词： machine learning Text Classification Unbalanced Classification Document Representation Opinion mining

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An ensemble pruning approach based on reinforcement learning in presence of multi-class imbalanced data 3

An ensemble pruning approach based on reinforcement learning...

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3rd international conference on Soft Computing for Problem Solving, SocProS 2013

作者： Abdi, Lida Hashemi, Sattar Department of Computer Science and Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9788132217701

In recent years, learning from imbalanced data sets has become a challenging issue in machine learning and data mining communities. This problem occurs when some classes of data have smaller number of instances than other classes. Multi-class imbalanced data sets have been pervasively observed in many real world applications. Many typical machine learning algorithms pose many difficulties dealing with these kinds of data sets. In this paper, we proposed an ensemble pruning approach which is based on Reinforcement learning framework. In effect, we were inspired by Markov Decision Process and considered the ensemble pruning problem as a one player game, and select the best classifiers among our initial state space. These selected classifiers which can produce a good ensemble model, are employed to learn from multi-class imbalanced data sets. Our experimental results on some UCI and KEEL benchmark data sets show promising improvements in terms of minority class recall, G-mean, and MAUC. © Springer India 2014.

关键词： Reinforcement learning

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Monitoring Distributed data Streams through Node Clustering

Monitoring Distributed Data Streams through Node Clustering

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10th international conference on machine learning and data mining (MLDM)

作者： Barouti, Maria Keren, Daniel Kogan, Jacob Malinovsky, Yaakov Univ Maryland Baltimore Cty Baltimore MD 21228 USA Univ Haifa Haifa Israel

ISBN: (纸本)9783319089799;9783319089782

Monitoring data streams in a distributed system is a challenging problem with profound applications. The task of feature selection (e.g., by monitoring the information gain of various features) is an example of an application that requires special techniques to avoid a very high communication overhead when performed using straightforward centralized algorithms. Motivated by recent contributions based on geometric ideas, we present an alternative approach that combines system theory techniques and clustering. The proposed approach enables monitoring values of an arbitrary threshold function over distributed data streams through a set of constraints applied independently on each stream and/or clusters of streams. The clusters are designed to adapt themselves to the data stream. A correct choice of clusters yields a reduction in communication load. Unlike many clustering algorithms that attempt to collect together similar data items, monitoring requires clusters with dissimilar vectors canceling each other as much as possible. In particular, sub-clusters of a good cluster do not have to be good. This novel type of clustering dictated by the problem at hand requires development of new algorithms, and the paper is a step in this direction. We report experiments on real-world data that detect instances where communication between nodes is required, and show that the clustering approach reduces communication load.

关键词： data streams convex analysis distributed system clustering

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The Human Face of Mobile

The Human Face of Mobile

引用

2nd IFIP TC5/8 international conference on ICT-EurAsia

作者： Mousannif, Hajar Khalil, Ismail Cadi Ayyad Univ LISI Lab Fac Sci Semlalia BP 2390 Marrakech 40000 Morocco Johannes Kepler Univ Linz Inst Telecooperat A-4040 Linz Austria

ISBN: (纸本)9783642550324;9783642550317

As the landscape around Big data continues to exponentially evolve, the "big" facet of Big data is no more number one priority of researchers and IT professionals. The race has recently become more about how to sift through torrents of data to find the hidden diamond and engineer a better, smarter and healthier world. The ease with which our mobile captures daily data about ourselves makes it an exceptionally suitable means for ultimately improving the quality of our lives and gaining valuable insights into our affective, mental and physical state. This paper takes the first exploratory step into this direction by using the mobile to process and analyze the "digital exhaust" it collects to automatically recognize our emotional states and accordingly respond to them in the most effective and "human" way possible. To achieve this we treat all technical, psycho-somatic, and cognitive aspects of emotion observation and prediction, and repackage all these elements into a mobile multimodal emotion recognition system that can be used on any mobile device.(1)

关键词： Emotion recognition Affective Computing Context pattern recognition machine learning Reality mining Intelligent Systems

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Bridging the language gap: learning distributed semantics for cross-lingual sentiment classification 3

Bridging the language gap: Learning distributed semantics fo...

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3rd CCF conference on Natural Language Processing and Chinese Computing, NLPCC 2014

作者： Zhou, Guangyou He, Tingting Zhao, Jun School of Computer Central China Normal University 152 Luoyu Road Wuhan430079 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9783662459232

Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a labelscare target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To address this challenge, previous work in the literature mainly relies on machine translation engines or bilingual lexicons to directly adapt labeled data from the source language to the target language. However, machine translation may change the sentiment polarity of the original data. In this paper, we propose a new model which uses stacked autoencoders to learn language-independent distributed representations for the source and target languages in an unsupervised fashion. Sentiment classifiers trained on the source language can be adapted to predict sentiment polarity of the target language with the language-independent distributed representations. We conduct extensive experiments on English-Chinese sentiment classification tasks of multiple data sets. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach. © Springer-Verlag Berlin Heidelberg 2014.

关键词： machine translation

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Extravaganza tutorial on hot ideas for interactive knowledge discovery and data mining in biomedical informatics

Extravaganza tutorial on hot ideas for interactive knowledge...

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2014 international conference on Brain Informatics and Health, BIH 2014

作者： Holzinger, Andreas Research Unit Human-Computer Interaction Institute for Medical Informatics Statistics and Documentation Medical University Graz Austria Institute for Information Systems and Computer Media Graz University of Technology Austria

ISBN: (纸本)9783319098906

Biomedical experts are confronted with "Big data", driven by the trend towards precision medicine. Despite the fact that humans are excellent at pattern recognition in dimensions of ≤ 3, most biomedical data is in dimensions much higher than 3, making manual analysis often impossible. Experts in daily routine are decreasingly capable of dealing with such data. Efficient, useable and useful computational methods, algorithms and tools to interactively gain insight into such data are a commandment of the time. A synergistic combination of methodologies of two areas may be of great help here: Human-Computer Interaction (HCI) and Knowledge Discovery/data mining (KDD), with the goal of supporting human intelligence with machine learning. Mapping higher dimensional data into lower dimensions is a major task in HCI, and a concerted effort including recent advances from graph-theory and algebraic topology may contribute to finding solutions. Moreover, much biomedical data is sparse, noisy and time-dependent, hence entropy is also amongst promising topics. This tutorial gives an overview of the HCI-KDD approach and focuses on 3 topics: graphs, topology and entropy. The goal of this intro tutorial is to motivate and stimulate further research. © 2014 Springer international Publishing.

关键词： data mining

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A feature set for streams and an application to high-frequency financial tick data 14

A feature set for streams and an application to high-frequen...

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3rd ASE international conference on Big data Science and Computing, BIGdataSCIENCE 2014

作者： Lyons, Terry Ni, Hao Oberhauser, Harald Oxford-Man Institute University of Oxford Eagle House Walton Well Rd Oxford United Kingdom

ISBN: (纸本)9781450328913

We propose a set of features to study the effects of data streams on complex systems. This feature set is called the the signature representation of a stream. It has its origin in pure mathematics and relies on a relationship between non-commutative polynomials and paths. This representation had already significant impact on algebraic topology, control theory, numerics for PDEs, stochastic analysis and the theory of rough paths;more recently first steps have been taken to apply such methods to the study of big data streams. We show that the signature representation can provide an efficient summary of a stream and its effects. We then show that it can be combined with standard tools from machine learning. After introducing the signature for streams and some theoretical background, we apply this approach to a challenging real-world example: high-frequency financial data streams. In this context, the streams are tickby-tick market data of a stock traded at the New York stock exchange NYSE and the effect of the stream is the profit and loss of complex investment strategies (i.e. a nonlinear functional of the stream). Our numerical results (applied to Thomson-Reuters tick data of several full trading days for IBM stocks) show that the signature of the price stream efficiently captures the necessary information to learn the return of an investment strategy. However, we emphasize that the underlying ideas are not limited to financial data streams and have the potential to be applied to many other areas in data mining where the non-commutative nature of streams is of importance, like text mining, bioinformatics or click history. © Copyright 2014 ACM.

关键词： Stochastic systems

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Using Latent Semantic Analysis to identify quality in use (QU) indicators from user reviews

Using Latent Semantic Analysis to identify quality in use (Q...

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international conference on Artificial Intelligence and pattern recognition, AIPR 2014

作者： Syn, Wendy Tan Wei How, Bong Chih Atoum, Issa Faculty of Computer Science and Information Technology University Malaysia Sarawak Kota Samarahan Sarawak94300 Malaysia

The paper describes a novel approach to categorize users' reviews according to the three Quality in Use (QU) indicators defined in ISO: effectiveness, efficiency and freedom from risk. With the tremendous amount of reviews published each day, there is a need to automatically summarize user reviews to inform us if any of the software able to meet requirement of a company according to the quality requirements. We implemented the method of Latent Semantic Analysis (LSA) and its subspace to predict QU indicators. We build a reduced dimensionality universal semantic space from Information System journals and Amazon reviews. Next, we projected set of indicators' measurement scales into the universal semantic space and represent them as subspace. In the subspace, we can map similar measurement scales to the unseen reviews and predict the QU indicators. Our preliminary study able to obtain the average of Fmeasure, 0.3627. © (2014) by Society of Digital Information and Wireless Communication (SDIWC). All rights reserved.

关键词： data mining

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A Time Effective Approach for Vector Similarity Measurement Based on Vector Addition

A Time Effective Approach for Vector Similarity Measurement ...

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3rd international conference on Civil and Architecture Engineering (ICCAE’2014);3rd international conference on Automation, Robotics and Mechanical Engineering (ICAMR’2014);3rd international conference on machine learning and Computer Science (IMLCS’2014);3rd international conference on Management, Humanity and Economics (ICMHE’2014);3rd international conference on Power, Control and VLSI Engineering (ICPCVE’2014)

作者： Wasiq Khan Ping Jiang Mohammed Bilal Speech Signal Processing University of Bradford Department of computer science intelligent systemsUniversity of HullUnited Kingdom Data Mining University of LeedsUnited Kingdom

pattern matching is one of the principal methodologies in speech signal processing and isolated word recognition. There are a number of similarity measures to find the similarity between the target words in an acquired speech by matching with template. The retrieved similarity result is dependent both on the feature vectors extraction technique and the distance metric produced by similarity measure. Short Time Fourier Transform is used for spectrogram re- trieval which is used for performance comparison of different simi- larity measures. In this paper, a novel similarity measure is proposed which is based on vector addition. It outperforms in terms of execu- tion time as compared to other similarity measures. We have done a detailed study of the performance of different similarity measurement techniques for a number of spectrograms on a large database of speech recordings. Manhattan distance, Euclidean distance, vector cosine angle distance, Bhattacharyya coefficients and posterior prob- ability measure are used for the performance comparison. The results show that this similarity measure outperforms as compare to all above similarity measures in terms of time complexity and similarity performance.

关键词： Signal Processing Speech recognition pattern Matching PPM Word Identification.

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