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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Chen, Yanqiu Sun, Peili Dalian Neusoft Inst Informat Dept Comp Sci Dalian Peoples R China Dalian Ocean Univ Econ Management Inst Dalian Peoples R China

ISBN: (纸本)9781728113074

For improve the efficiency and accuracy of Chinese text categorization, this paper presents a new Chinese text classier, in which word segmentation system is based on forward scan corpus, and word frequency statistics method is different in the training stage and the test stage, after that, a novel feature selection is proposed according to word frequency, mutual information and classificatory information, Finally a fast Bayes theory classier is designed. Experiments prove this classier is simple and effective.

关键词： Chinese text categorization feature selection feature weighting classification algorithm

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Improved Framework for Breast Cancer Prediction Using Frequent Itemsets Mining for Attributes Filtering

Improved Framework for Breast Cancer Prediction Using Freque...

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International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Sinha, Ankita Sahoo, Bhaswati Rautaray, Siddharth Swamp Pandey, Manjusha KIIT Deemed Univ Sch Comp Engn Bhubaneswar Odisha India

ISBN: (纸本)9781538681138

Data Mining is applicable for pulling up some new information by analyzing the database. It is also used for prediction based on real and actual current data. Breast cancer is a very harmful disease which effects badly to ones social, physical life and also effects mentally. This paper focuses on the attribute filtering techniques i.e frequent itemsets mining with the intention to find the essential and relevant attribute from the Wisconsin breast cancer dataset and classification algorithmic program like SVM, Naive Bayes, k-NN, Decision Tree comparison is done with attribute filtering. SVM produces beat result among all the classifier with attribute filtering.

关键词： Data mining Breast cancer Z-score normalization Frequent itemsets mining classification algorithm R analytical tool Prediction

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CRISP - An Interruption Management algorithm based on Collaborative Filtering 14

CRISP - An Interruption Management Algorithm based on Collab...

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32nd Annual ACM Conference on Human Factors in Computing Systems (CHI)

作者： Shrot, M. Tammar Rosenfeld, Avi Golbeck, Jennifer Kraus, Sarit Shamoon Coll Engn Dept Software Engn IL-84100 Beer Sheva Israel Jerusalem Coll Technol Dept Ind Engn IL-9116001 Jerusalem Israel Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Bar Ilan Univ Dept Comp Sci IL-5290002 Ramat Gan Israel

ISBN: (纸本)9781450324731

Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often require long training periods that make them unsuitable for online collaborative environments where new users frequently participate. In this paper, we present a novel synthesis between Collaborative Filtering methods and machine learning classification algorithms to create a fast learning algorithm, CRISP. CRISP exploits the similarities between users in order to apply data from known users to new users, therefore requiring less information on each person. Results from user studies indicate the algorithm significantly improves users' performances in completing the task and their perception of how long it took to complete each task.

关键词： Interruption Management (Cost Estimation) Collaborative Filtering classification algorithm

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Hand posture recognition using jointly optical flow and dimensionality reduction

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2013年第1期2013卷 1-22页

作者： Boughnim, Nabil Marot, Julien Fossati, Caroline Bourennane, Salah Aix Marseille Univ CNRS UMR 7249 Fresnel InstEcole Cent MarseilleDU St Jerome F-13397 Marseille 20 France

Hand posture recognition is generally addressed by using either YCbCr (luminance and chrominance components) or HSV (hue, saturation, value) mappings which assume that a hand can be distinguished from the background from some colorfulness and luminance properties. This can hardly be used when a dark hand, or a hand of any color, is under study. In addition, existing recognition processes rely on descriptors or geometric shapes which can be reliable;this comes at the expense of an increased computational complexity. To cope with these drawbacks, this paper proposes a four-step method recognition technique consisting of (i) a pyramidal optical flow for the detection of large movements and hence determine the region of interest containing the expected hand, (ii) a preprocessing step to compute the hand contour while ensuring geometric and illumination invariance, (iii) an image scanning method providing a signature which characterizes non-star-shaped contours with a one-pixel precision, and (iv) a posture classification method where a sphericity criterion preselects a set of candidate postures, principal component analysis reduces the dimensionality of the data, and Mahalanobis distance is used as a criterion to identify the hand posture in any test image. The proposed technique has been assessed in terms of its performances including the computational complexity using both visual and statistical results.

关键词： Hand posture Contour signature classification algorithm Optical flow Principal component analysis

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Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification

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DIGITAL HEALTH 2023年 9卷

作者： Choi, Junggu Kwon, Seohyun Park, Sohyun Han, Sanghoon Yonsei Univ Yonsei Grad Program Cognit Sci Seoul South Korea Munice Inc Vienna VA USA Yonsei Univ Underwood Int Coll Econ Seoul South Korea Yonsei Univ Dept Psychol Seoul South Korea Yonsei Univ Dept Psychol Seoul 03722 South Korea Yonsei Univ Yonsei Grad Program Cognit Sci Seoul 03722 South Korea

BackgroundSleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. MethodsTo compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. ResultsThe combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. ConclusionWe determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies.

关键词： Sleep stage classification polysomnography biosignal machine learning classification algorithm

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Optical biomarkers for detection of malignant tissue using Digital Holographic Microscopy 2

Optical biomarkers for detection of malignant tissue using D...

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Conference on Advances in Microscopic Imaging II

作者： Calin, Violeta L. Mihailescu, Mona Costea, Radu V. Dumitru, Adrian Patrascu, Oana-Maria Brehar, Felix Petrescu, George Lisievici, Mihai Comanescu, Brindus Grigore, Viorela N. Butmalai, Dan Savopol, Tudor Moisescu, Mihaela G. Carol Davila Univ Med & Pharm 8 Eroii Sanitari Blvd Bucharest 050474 Romania Politehn Univ 313 Spl Independentei Bucharest 060042 Romania Univ Emergency Hosp Bucharest 169 Spl Independentei Bucharest 050098 Romania Bagdasar Arseni Clin Emergency Hosp 12 Berceni St Bucharest 041915 Romania Optoelect SA 409 Atomistilor St Magurele 077125 Romania

ISBN: (数字)9781510628465

ISBN: (纸本)9781510628465

The histopathological diagnosis in malignances requests well trained specialists and multi-step operational procedures for sample preparation. Faster and more objective evaluation protocols should be implemented to give support to the pathologists. The Quantitative Phase Imaging based methods are biological-proved to be efficient in revealing important characteristics of the living structures without any labeling. These can be further exploited for an automatic evaluation of complex tissues. Using an off-axis Digital Holographic Microscopy setup, biopsies of two histological origins: cerebral (grade II glioma and grade IV glioblastoma) and colonic malignancies (dysplastic and malignant colonic adenomatous polyps), were investigated. Various parameters of quantitative phase shift maps (QPMs) were computed (mean, variance, median, kurtosis, skewness, energy, entropy). The possibility of automatic discrimination of tumor tissues having different structural complexity and presenting various malignancy grades was evaluated using supervised machine learning algorithms. The analysis of phase shift maps has successfully discriminated between levels of malignancy with high statistical confidence in the case of gliomas. Moreover an algorithm with the ability to classify the tissue biopsies in different malignant stages using parameters based on QPMs has been implemented on glioma tissues having a high level of homogeneity. In case of colonic polyps, the heterogeneity of the multilayered tissue demanded QPMs analysis to be performed on selected area of interest even though some statistical differences were obtained for global evaluation of phase shift distributions. In case of colonic polyps, for a good accuracy of classification algorithm a larger library of QPMs is under construction.

关键词： Digital Holographic Microscopy quantitative phase maps colonic adenomatous polyps glioblastomas classification algorithm

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Cost-sensitive Classifiers in Credit Rating A Comparative Study on P2P Lending 7

Cost-sensitive Classifiers in Credit Rating A Comparative St...

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7th International Conference on Computers Communications and Control (ICCCC)

作者： Wang, Haomin Kou, Gang Peng, Yi Univ Elect Sci & Technol China Sch Management & Econ Chengdu Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538619346

In credit rating, borrowers are classified into some grades, based on their past credit experience, to recognize potential borrowers who are of different probability of default. classification algorithms are commonly used, which is important to reduce loss for banks or investors. Since multiple classes are imbalanced, as well as losses of misclassification across classes are not uniform, multi-class cost-sensitive classifiers should be paid more attention in credit rating. Based on existing literatures, in this paper, we review several cost-sensitive classifiers and compare them with three assumed cost matrices. The empirical study utilizes data collected from Lending Club which is the largest U.S. P2P loan platform. The results are aimed at giving insights on cost-sensitive classifiers with various performances in credit rating, which are concerned by researchers and practitioners of credit lending.

关键词： data mining cost-sensitive learning classification algorithm credit rating P2P lending

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The Advance of Support Tensor Machine 16

The Advance of Support Tensor Machine

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16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Xiang, Yi He, Jing Wu, LiWen Jiang, Qian Jin, Xin Yao, Shaowen Yunnan Univ Sch Software Kunming Yunnan Peoples R China

ISBN: (纸本)9781538658864

In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.

关键词： Support Tensor Machine Tensor Decomposition classification algorithm

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Predicting Customer Behavior with Combination of Structured and Unstructured Data 3

Predicting Customer Behavior with Combination of Structured ...

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3rd International Conference on Science and Sustainable Development (ICSSD)

作者： Afolabi, Ibukun T. Worlu, Rowland E. Adebayo, O. P. Jonathan, Oluranti

Presently, there are numerous e-marketing and m-marketing mediums that exist such as YouTube, SMS, What Sapp, Google, twitter, yahoo, Facebook, LinkedIn, email and personal blogs. These mediums are beginning to be used for marketing purposes, particularly by the SMEs in Nigeria. The aim of this research is to address the problem of deciding which of the mediums mentioned above is mostly appropriate to target customer of a particular SME and also to discover the type of data that is most appropriate for analysis in making this decision. In order to achieve this, data was gathered by administering questionnaires and pre-processed based on structured and unstructured data sources. The J48 decision tree classification algorithm was used to mine the data, relevant predictions were made from the structured and unstructured data and the results were evaluated. The results revealed that predicting from unstructured data expresses more of popular opinion, so decision can start from unstructured results and be fined tuned or validated with predicting from structured data. Though structured prediction appears to be better than unstructured, unstructured prediction is still very valuable in situations where there are no structured data such as analysing text messages. Also, Models developed for predicting customer behaviour as regards the marketing channels studied, will form the foundation for marketing decision making, in small and medium businesses in Nigeria.

关键词： Data Mining classification algorithm Marketing e-marketing m-marketing Structured data Unstructured data

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Application of Ant Colony Optimization-SVM in Fault Diagnosis for Rectifier Circuit

Application of Ant Colony Optimization-SVM in Fault Diagnosi...

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2nd IEEE International Conference on Information and Financial Engineering (ICIFE)

作者： Xu Binghui Taizhou Vocat & Tech Coll Taizhou 318000 Peoples R China

ISBN: (纸本)9781424469284

Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper. The experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental object. The testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.

关键词： rectifier circuit ant colony optimization classifier fault diagnosis classification algorithm

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