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3rd international conference on Smart City Applications (SCA')

作者： Saoud, Hajar Ghadi, Abderrahim Ghailani, Mohamed Abdelhakim, Boudhir Anouar UEA LIST Lab Tangier Morocco UEA LabTIC Lab Tangier Morocco

ISBN: (纸本)9781450365628

Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making. The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor. Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector machine (SVM), k-nearest neighbors algorithm (Kim), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.

关键词： Brest cancer diagnostic machines learning algorithms classification WEKA

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An Approach to Classify Human Activities in Real-time from Smartphone Sensor data 8

An Approach to Classify Human Activities in Real-time from S...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Ahmed, Masud Das Antar, Anindya Ahadt, Atiqur Rahman Univ Dhaka Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781728107882

According to the scenario of the last few decades, sensor-based human activity recognition has gained considerable research attention due to its novel applications in health care, machine learning, human-computer interaction, etc. In this research, we have investigated several data pre-processing methods to recognize static and dynamic activities more accurately based on smartphone sensor data. The addition of magnitude and jerk-based features ensure the recognition of activities independent of the orientation of the smartphone in daily life. Besides, we have proposed a new feature namely Average peak-trough-distance (APTD), which ensures better recognition accuracy. We have also discussed a sliding window technique without overfitting, which utilizes observation of test data from previous windows with 50% overlapping for the detection of activity in real-time. Moreover, we have evaluated our proposed method showing a comparison between four classifiers. We have found the best accuracy for HASC2010corpus dataset using our proposed feature along with the multi-class Support Vector machine, where we have utilized the Grid Search Cross Validation optimization technique to tune the hyper-parameters. We have also shown that the proposed approach outperforms the naive methods for the detection of activity using smartphones more precisely in real-time applications.

关键词： Activity recognition action recognition average peak-trough-distance classification smartphone sensors

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A Neuro-Genetic Framework for pattern recognition in Complex Systems

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FUNDAMENTA INFORMATICAE 2008年第2期87卷 207-226页

作者： Bandini, Stefania Vanneschi, Leonardo Wuensche, Andrew Shehata, Alessandro Bahgat Univ Milano Bicocca Complex Syst & Artificial Intelligence CSAI Res C Dept Informat Syst & Commun DISCo I-20126 Milan Italy Univ Sussex Dept Informat Sch Sci & Technol Brighton BN1 9RH E Sussex England

This paper presents a general framework to automatically generate rules that produce given spatial patterns in complex systems. The proposed framework integrates Genetic Algorithms with Artificial Neural Networks or Support Vector machines. Here, it is tested on a well known 3-values, 6-neighbors, k-totalistic cellular automata rule called the "burning paper" rule. Results are encouraging and should pave the way for the use of the proposed framework in real-life complex systems models.

关键词： Complex Systems Cellular Automata pattern recognition Automatic Rule Generation machine learning Genetic Algorithms Artificial Neural Networks Support Vector machines

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The 3rd international Workshop on machine learning on Graphs (MLoG) 23

The 3rd International Workshop on Machine Learning on Graphs...

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16th international conference on Web Search and data mining

作者： Derr, Tyler Ma, Yao Rozemberczki, Benedek Shah, Neil Pan, Shirui Vanderbilt Univ Nashville TN 37235 USA New Jersey Inst Technol Newark NJ USA Isomorph Labs London England Snap Inc Santa Monica CA USA Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450394079

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd international Workshop on machine learning on Graphs (MLoG)(1), held in conjunction with the 16th ACM conference on Web Search and data mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： machine learning on Graphs

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Tell Me Why - A Systematic Literature Review on Outlier Explanation for Tabular data 3

Tell Me Why - A Systematic Literature Review on Outlier Expl...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Nonnenmacher, Jakob Holte, Nils-Christoph Gomez, Jorge Marx University of Oldenburg Oldenburg Germany

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Outlier explanation approaches are used to support analysts in investigating outliers, especially those detected by methods which are not intuitively interpretable such as deep learning or ensemble approaches. There have been several studies on outlier explanation and visualization in the last years. Nonetheless, there has been no overarching survey of existing outlier explanation approaches. We investigate what kind of explanation approaches exist for tabular data and how they can be characterized. For this, we perform a literature review of 418 papers uncovering 26 existing studies which we analyze and categorize according to: approach, model dependence, supported data, output and evaluation. Based on our categorization, we identify trends within outlier explanation. This includes the limited number of underlying approaches and the lack of a common evaluation method. We are also able to highlight avenues for further research such as utilizing different datasets for evaluation or exploring visualization approaches. © 2022 IEEE.

关键词： Anomaly detection

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Comparing Feature learning Methods for Human Activity recognition: Performance study in new user scenario 8

Comparing Feature Learning Methods for Human Activity Recogn...

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作者： Lago, Paula Inoue, Sozo Kyushu Inst Technol Fac Engn Dept Basic Sci Kitakyushu Fukuoka Japan Kyushu Inst Technol Fac Engn Grad Sch Biol Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781728107882

Designing robust features for human activity recognition (HAR) that performs well across a wide range of users is a hard task. Therefore, more attention is being given to feature learning techniques, to automatically learn features from raw data. In this paper, we present a comparison study among feature learning methods for HAR. Using accelerometer data, we compare four methods for feature learning from raw-sensor data (PCA-based, clustering, matrix factorization, and LSTM networks) to the traditional hand-crafted feature extraction method. We focus on the performance degradation when each model is evaluated using a new user. According to our results, features learned with Principal Component Analysis are the more robust to the new user scenario. Our results evidence the importance of evaluation in unseen user, since the performance difference compared to a random split testing is big.

关键词： feature learning activity recognition accelerometer smartphones

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WLCSSLearn: learning Algorithm for Template Matching-based Gesture recognition Systems 8

WLCSSLearn: Learning Algorithm for Template Matching-based G...

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作者： Ciliberto, Mathias Cuspinera, Luis Ponce Roggen, Daniel Univ Sussex Wearable Technol Lab Brighton E Sussex England Univ Sussex Brighton E Sussex England

ISBN: (纸本)9781728107882

Template matching algorithms are well suited for gesture recognition, but unlike other machine learning approaches there are no established methods to optimize their parameters. We present WLCSSLearn: an optimization approach for the WarpingLCSS algorithm based on a genetic algorithms. We demonstrate that WLCSSLearn makes the optimization procedure automatic, fast and suitable for new recognition problems even when there is no a-priori knowledge about suitable range of parameter values. We evaluate WLCSSLearn on three different datasets of gestures. We demonstrated that our method increased the accuracy and F1 score up to 20% compared to previous literature.

关键词： component formatting style styling insert

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A Brief Review of Unsupervised learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems 3

A Brief Review of Unsupervised Learning Algorithms for Zero-...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Oluwadare, Sunkanmi ElSayed, Zag Adekoya, Oluwaseun Univ Cincinnati Dept Informat Technol Cincinnati OH 45221 USA Univ Cincinnati Dept Mech Engn Cincinnati OH 45221 USA

ISBN: (纸本)9798350372977;9798350372984

This research paper presents a brief review of ten popular unsupervised algorithms widely utilized in pattern recognition publications. The algorithms are assessed based on their popularity, strengths, limitations, and resource requirements. Considering these factors, we propose two most-preferred algorithms suitable for adoption in IDS (Intrusion Detection Systems) to address the problems associated with Zero Day exploits or attacks. Our review of the surveyed algorithms facilitated the recommendation of specific algorithms that can enhance IDS capabilities in detecting and mitigating Zero-Day attacks and anomalous intrusion attempts. These algorithms leverage unsupervised learning techniques to overcome the limitations of traditional signature-based approaches. By incorporating these algorithms, IDS can better handle sophisticated and evolving attacks that often evade detection. In conclusion, this research provides valuable insights into the strengths, limitations, and resource requirements of popular unsupervised algorithms used in pattern recognition. It highlights the potential of adopting these algorithms in IDS systems to bolster their ability to detect and respond to Zero-Day attacks. By recommending the integration of these algorithms, we contribute to the development of intelligent IDS solutions that can adapt to dynamic threat landscapes.

关键词： Intrusion Detection Zero-Day Attacks machine learning Unsupervised learning pattern recognition

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A Geometric Algorithm for learning Oblique Decision Trees

A Geometric Algorithm for Learning Oblique Decision Trees

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3rd international conference on pattern recognition and machine Intelligence

作者： Manwani, Naresh Sastry, P. S. Indian Inst Sci Bangalore 12 Karnataka India

ISBN: (纸本)9783642111631

In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.

关键词： Geometry

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An extensible service oriented distributed data mining framework

An extensible service oriented distributed data mining frame...

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3rd international conference on machine learning and Applications

作者： Kumar, A Kantardzic, M Ramaswamy, P Sadeghian, P Univ Louisville Dept Comp Sci & Comp Engn JB Speed Sch Engn Louisville KY USA

ISBN: (纸本)0780388232

This paper discusses a new approach for developing a service-oriented infrastructure for distributed data mining applications. The proposed architecture hides the complexity of implementation details and enables users to perform data mining in a utility-like fashion. The service-oriented architecture provides an autonomic data mining framework where self-describing data mining services can be automatically discovered on the Internet. Moreover, this structure allows for the implementation of data mining algorithms for processing data on more than one site in a distributed manner. The performance of the proposed distributed data mining framework is compared to a standard data mining approach to demonstrate its effectiveness.

关键词： data mining

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