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智能系统学报 2015年第1期10卷 11-11页

June 26 to 29,2015,Beijing,China the Sixth international conference on Swarm Intelligence and the Second BRICS Congress on Computational Intelligence(ICSICCI'2015)(http://***)will be jointly held in Beijing,China,from June 26 to 29,*** theme of the ICSI-CCI'2015is"SERVING OUR SOCIETY AND LIFE WIth INTELLIGENCE".With the advent of big

关键词：智能系统发展现状计算机技术技术创新

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Optimization of cost sensitive models to improve prediction of molecular functions 6

Optimization of cost sensitive models to improve prediction ...

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6th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013

作者： García-López, SebastiáN Jaramillo-Garzón, Jorge Alberto Castellanos-Dominguez, German Signal Processing and Recognition Group Universidad Nacional de Colombia Campus la Nubia Km 7 vía al Magdalena Manizales Colombia Grupo de Automática y Electrónica Instituto Tecnológico Metropolitano Cll 54A No 30-01 Medellín Colombia

ISBN: (纸本)9783662444849

the prediction of unknown protein functions is one of the main concerns at field of computational biology. this fact is reflected specifically in the prediction of molecular functions such as catalytic and binding activities. this, along with the massive amount of information has made that tools based on machine learning techniques have increase their popularity in the last years. However, these tools are confronted to several problems associated to the treated data, one of them is the learning with large imbalance between their categories. there exist several techniques to overcomes the class imbalance, but most of them present many weakness that difficult the obtaining of reliable results. Moreover, models based on cost sensitive learning seems to be a good choice to deal with imbalance data, yet, the obtaining of a optimal cost matrix still remains an open issue. In this paper, a methodology to calculate a optimal cost matrix for models based on cost sensitive learning is proposed. the results show the superiority of this approach compared with several techniques in the state of the art regarding to class imbalance. Tests were applied to prediction of molecular functions in Embryophyta plants. ©Springer-Verlag Berlin Heidelberg 2014.

关键词： Forecasting

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Support Vector machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset

Support Vector Machine-Based Fault Diagnosis of Power Transf...

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the 3rd international conference on signal and Image processing(CSIP 2014) & the 6th international conference on Computational Intelligence and Software Engineering(CiSE 2014)

作者： Zahriah Binti Sahri Rubiyah Binti Yusof Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Malaysia Japan International Institute of Technology Universiti Tekonologi MalaysiaKuala Lumpur Malaysia

Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis(DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. thereafter, using these imputed datasets as inputs, this study applies Support Vector machine(SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.

关键词： Missing Values,Dissolved Gas Analysis,Support Vector machine,k-Nearest Neighbors

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A New Multi-Class WSVM Classification to Imbalanced Human Activity Dataset

A New Multi-Class WSVM Classification to Imbalanced Human Ac...

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the 6th international conference on Computer Research and Development

作者： M'hamed B.Abidine Belkacem Fergani Speech Communication & Signal Processing Laboratory Faculty of Electronics and Computer Sciences USTHB

this paper is concerned with the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classification algorithms. To deal this problem, we propose a new version of the multi-class Weighted Support Vector machines(WSVM) method to perform automatic recognition of activities in a smart home environment. then, we compare this approach with CRF, k-NN and SVM considered as the reference methods. Our experimental results carried out on various real world imbalanced datasets show that the new WSVM is capable of solving the class imbalance problem by improving the class accuracy of activity classification compared to other methods.

关键词： Imbalanced data support vectors machines activity recognition machine learning

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Unsupervised Decoder Initialization for Brain-machine Interfaces Using Neural State Space Dynamics

Unsupervised Decoder Initialization for Brain-Machine Interf...

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6th international IEEE EMBS conference on Neural Engineering (NER)

作者： Badreldin, Islam Southerland, Joshua Vaidya, Mukta Eleryan, Ahmed Balasubramanian, Karthikeyan Fagg, Andrew Hatsopoulos, Nicholas Oweiss, Karim Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9781467319690

One of the key elements in the design of neuromotor Brain-machine Interfaces (BMIs) is the neural decoder design. In a biomimetic approach, the decoder is typically trained from concurrent recordings of neural and kinematic or motor imagery data. the non-availability of the latter data imposes a practical problem for patients with lost motor functions. An alternative approach is a biofeedback approach in which subjects are encouraged to 'learn' an arbitrary mapping between neural activity and the external end effector. In this work, we propose an unsupervised decoder initialization scheme to be used in the biofeedback approach that alleviates the need for synchronized kinematic or motor imagery data for decoder training. the approach is totally unsupervised in that the recorded neural activity is directly used as training data for a decoder designed to provide 'desirable' features in the decoded control signal. the decoder is trained from 'spontaneous' neural data when the BMI subject is not engaged in any behavioral task, and we demonstrate its ability to generalize to neural data collected when the subject is in a different behavioral state.

关键词： bioelectric potentials biomedical electrodes brain brain-computer interfaces encoding feedback kinematics medical signal detection medical signal processing neurophysiology synchronisation unsupervised learning

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Automatic Han Chinese Folk Song Classification Using the Musical Feature Density Map

Automatic Han Chinese Folk Song Classification Using The Mus...

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6th international conference on signal processing and Communication Systems (ICSPCS)

作者： Khoo, Suisin Man, Zhihong Cao, Zhenwei Swinburne Univ Technol Fac Engn & Ind Sci Hawthorn Vic 3122 Australia

ISBN: (纸本)9781467323932;9781467323925

Automatic music classification has received increased attention during the past decade. A system employing artificial neural network (ANN) techniques for the classification of Han Chinese folk songs is presented in this paper. Melodies of Han Chinese folk songs are machine-classified according to the different geographical region of the folk song's origin. Both audio and symbolic representations of music are studied. A novel encoding method called musical feature density map (MFDMap) is proposed to encode the symbolic musical features extracted from each folk song for machine classification. Our simulations demonstrate that the regularized extreme learning machine (R-ELM) classifier can achieve 72% classification accuracy using the MFDMap with three of the four suggested symbolic features.

关键词： musical feature density map music classification extreme learning machine Han Chinese folk song artificial neural network

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Card Fraud Detection by Inductive learning and Evolutionary Algorithm

Card Fraud Detection by Inductive Learning and Evolutionary ...

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6th international conference on Genetic and Evolutionary Computing (ICGEC)

作者： Lei, Liang Huawei Beijing R&D Inst Beijing Peoples R China

ISBN: (纸本)9780769547633;9781467321389

Many fraud analysis system has been in the hearts of their rule based engine to generate an alert suspicious behavior. the rules system is usually based on expert knowledge. Automatic rules of the goal were to use ever found cases of fraud and lawful use to search new patterns and rules to help distinguish between the two. In this paper, we proposed an evolutionary inductive learning from credit card transaction data found rules, combined with genetic algorithm and cover algorithm. Covering algorithm will be a separate-conquer method inductive rule learning. Genetic algorithm embedded in the main circuit of the covering algorithm for rule search. Focus on the selection of attributes and define derived attributions to catch up time-dependent fraudulent features. Measuring complex factors is to avoid the phenomenon of over fitting introduction. From the millions of data with billions of steps computational understanding of unknown concept in need of advanced software development technology to support the implementation of the algorithm in a reasonable execution time. the system has been applied in the real world of credit card transaction data to distinguish between legitimate fraudulent transactions

关键词： Credit card fraud detection inductive learning genetic algorithms data mining machine learning

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Multiple feature selectoin for pattern recognition using ID3 ensemble system

Multiple feature selectoin for pattern recognition using ID3...

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6th international conference on Genetic and Evolutionary Computing (ICGEC)

作者： Nakatsu, Koichiro Furuta, Hitoshi Takahashi, Kyousuke Ishibashi, Ken Yasuda, Shigeki Osaka Jonan Womens Jr Coll Sumiyoshi Ku Higashi Osaka Japan Kansai Univ Takatsuki Osaka Japan

ISBN: (纸本)9780769547633;9781467321389

In pattern recognition, feature selection is a quite important process for constructing practical systems. However, because there are many features as candidates to recognize object, it is difficult to select appropriate features for pattern recognition systematically. the previous research proposed a pattern recognition method using the ensemble system based on fuzzy classifier for multiple feature selection. However, this method can not apply for the problems with many input vectors because it takes a lot of time for learning when the number of the input vector increases. In this study, an attempt is made to overcome the problem by introducing ID3 (Iterative Dichotomizer 3) with classifiers consisting of many feature vectors. ID3 constructs a decision tree for multiple feature selection with the results obtained from classifiers based on each feature. therefore, it is possible to select appropriate features applied many input vectors. Several benchmark problems are presented to demonstrate the efficiency and applicability of the proposed method.

关键词： pattern recognition machine learning feature selection ID3

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Automatic modulation classification in wireless disaster area emergency network (W-DAEN)

Automatic modulation classification in wireless disaster are...

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2011 6th international ICST conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2011

作者： Rahman, Md. Abdur Haniz, Azril Kim, Minseok Takada, Jun-Ichi Department of International Development Engineering Graduate School of Science and Engineering Tokyo Institute of Technology Meguro Tokyo 152-8552 Japan

ISBN: (纸本)9781936968190

Post-disaster situation requires quick and effective rescue efforts by the first responders. Generally the rescue teams use wireless radios for intra-agency communications. Lack of collaboration among different rescue agencies may create interference among the emergency radios. Identification of some physical parameters of these active radios is necessary for collaboration. Carrier frequency and bandwidth can be estimated by spectrum sensing, whereas modulation classification requires further signal processing and classification operations. processing speed and performance of the classification system can be controlled by appropriate selection of signal parameters, signal processing techniques and the classification algorithms. A wireless disaster area emergency network (W-DAEN) can be installed in the disaster area to detect and capture data (time samples) of the occupied frequencies. this study consists of some simulation results of a machine learning based cooperative automatic modulation classification technique by using six unique features. the classification performance and processing time of the proposed algorithm is quite satisfactory for real-time classification system. © 2011 ICST.

关键词： Modulation

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Automatic modulation classification in wireless disaster area emergency network (W-DAEN)

Automatic modulation classification in wireless disaster are...

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international conference on Cognitive Radio Oriented Wireless Networks and Communications

作者： Md. Abdur Rahman Azril Haniz Minseok Kim Jun-ichi Takada Department of International Development Engineering Graduate School of Science and Engineering Tokyo Institute of Technology Meguro Tokyo Japan

关键词： Frequency shift keying Phase shift keying Feature extraction Time frequency analysis Estimation

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