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اخبار و اعلانات
آمار
| تعداد نشریات | 60 |
| تعداد شماره‌ها | 2,020 |
| تعداد مقالات | 16,229 |
| تعداد مشاهده مقاله | 42,398,313 |
| تعداد دریافت فایل اصل مقاله | 24,277,851 |
رویکردی نوین به منظور کشف و تجزیه وتحلیل دانش پدیده های استثنایی با استفاده از داده کاوی | ||
| مطالعات مدیریت کسب و کار هوشمند | ||
| مقاله 1، دوره 3، شماره 12، شهریور 1394، صفحه 1-20 اصل مقاله (725.04 K) | ||
| نویسندگان | ||
| مسعود عابسی1؛ الهه حاجی گل یزدی2؛ حسن حسینی نسب3؛ محمد باقر فخرزاد1 | ||
| 1استادیار گروه مهندسی صنایع، دانشکده فنی مهندسی، دانشگاه یزد، یزد، ایران | ||
| 2دانشجوی دکتری مهندسی صنایع، دانشکده فنی مهندسی، دانشگاه یزد، یزد، ایران | ||
| 3دانشیار گروه مهندسی صنایع، دانشکده فنی مهندسی، دانشگاه یزد، یزد، ایران | ||
| چکیده | ||
| منطق یادگیری ازاستثنائات چالشی قابل توجه در حوزه دادهکاوی است. استثنائات پدیدههای نادری هستند که رفتاری مثبت و متفاوت از الگوهای اصلی و مورد انتظار موجود در پایگاهداده از خود بروز می دهند. ایجاد چارچوبی کارا برای افزایش اطمینان به پدیدههای استثنایی در کشف دانش و یادگیری موثر از آن حائز اهمیت است. در این پژوهش، الگویی بر اساس تئوری استثنائات و تئوری اطلاعات ارائه شده است تا چالش‌های پیشروی دادهکاوی داده‌های استثنایی را برطرف نماید. نخست از تابع آنتروپی رنی برای شناسایی استثنائات استفاده و سپس با بکارگیری رویکرد یادگیری پایین به بالا بر مبنای الگوریتم پیشنهادی RISE ارتقا یافته، قوانین حاکم بر بروز رفتار استثنایی استخراج میگردد. به منظور تعیین کارایی مدل پیشنهادی، کشف سهام استثنایی و یادگیری رفتار آنها مورد بررسی قرار گرفته است. از مجموع 1334 سهم مورد بررسی 36 سهم رفتار استثنایی داشته اند که رفتار آن ها در قالب سه قانون مشخص شده است. ارجحیت نتایج حاصل از مدل پیشنهادی نسبت به نتایج بدست آمده از بکارگیری الگوریتمهای معمول یادگیری بیانگرکارایی مدل ارائه شده است. است. | ||
| کلیدواژه‌ها | ||
| دادهکاوی؛ تئوری استثنائات؛ تئوری اطلاعات؛ الگوریتم یادگیری پایین به بالا؛ پدیده های استثنایی | ||
| مراجع | ||
|
Albanis G. Batchelor R. Combining heterogeneous classifiers for stock selection, Intelligent Systems in Accounting, Finance and Management, vol. 15, no. 1-2, pp. 1-27, 2007.
Burez J. Van den Poel D. Handling class imbalance in customer churn prediction, Expert Systems with Applications 36, 4626–4636, 2009
Califf M. E. Mooney R. J. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction, Journal of Machine Learning Research 4,177-210, 2003.
Cao L. Zhao Y. Zhang C. Mining Impact-Targeted Activity Patternsin Imbalanced Data, IEEE Transactions on knowledge and data engineering, Vol. 20, NO. 8, 2008.
Chawla N. V. Japkowicz N. lcz A. K. Editorial: Special Issue on Learning from Imbalanced Data Sets, Sigkdd Explorations, 6(1):1–6, 2004.
Chen M. C. Chen L. S. Hsu C. C. Zeng W. R. An information granulation based data mining approach for classifying imbalanced data, Information Sciences 178, 3214–3227, 2008.
Clark E. Exploiting stochastic dominance to generate abnormal stock returns, Journal of Financial Markets 20, 20–38, 2014.
Cover T. M. Thomas J. A. Entropy, Relative Entropy and Mutual Information; Elements of Information Theory, ISBN 0-471-06259-6-pp: 12-49, 1991.
Duong T. V. Bui H. H. Phung D. Q. Venkatesh S. Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005.
García V. Sánchez J.S. Mollineda R.A. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowledge-Based Systems 25, 13–21, 2012.
Gong R.S. A Segmentation and Re-balancing Approach for Classification of Imbalanced Data, PHD theses, University of Cincinnati, 2010.
Hoffman M. L. Moral internalization: Current theory and research, In L. Berkowitz (Ed.), Advances in experimental social psychology10, 85-133, 1977.
Hu D. H. Zhang X. X. Yin J. Zheng V. W. Yang Q. Abnormal Activity Recognition Based on HDP-HMM Models, the Twenty-First International Joint Conference on Artificial Intelligence, 2009.
Japkowicz, N., The class imbalance problem: Significance and strategies, the international conference on artificial intelligence: Special track on inductive learning, 2000.
Joshi M. V, Learning Classifier Models for Predicting Rare Phenomena, PhD thesis, University of Minnesota, Twin Cites, Minnesota, USA, 2002.
Kim Y. Sohn S.Y. Stock fraud detection using peer group analysis, Expert Systems with Applications 39, 8986–8992, 2012.
Kou Y, Abnormal Pattern Recognition in Spatial Data, PHD theses, Faculty of Virginia Polytechnic Institute and State University, 2006.
Li X. Rao F. Outlier Detection Using the Information Entropy of Neighborhood Rough Sets, Journal of Information & Computational Science, 3339–3350, 2012.
McCarthy J. Applications of circumscription to formalizing common-sense knowledge,Artificial Intelligence 28, 89-116, 1986.
Nagi J. An intelligent system for detection of non-technical losses in Tanaga National Berhad (TNB) Malaysia low voltage distribution network, PhD Thesis, Tenaga national university,2009.
QamarU.Automated Entropy Value Frequency (AEVF) Algorithm for OutlierDetection in Categorical Data, Recent Advances in Knowledge Engineering and Systems Science,28-35, 2011.
Reiter R. A Theory of Diagnosis from First Principles, Artificial Intelligence 32, 57-95, 1987.
Setyohadi D. B. Abu Bakar A. Othman Z.A. Rough K-means Outlier Factor Based on Entropy Computation, Research Journal of Applied Sciences, Engineering and Technology 8(3): 398-409, 2014.
Weiss G. Mining with rarity: A unifying framework. SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets,6(1):7–19, 2004.
Xiang T. Gong S. Video Behavior Profiling for Anomaly Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(5), 893–908, 2008.
Albanis G. Batchelor R. Combining heterogeneous classifiers for stock selection, Intelligent Systems in Accounting, Finance and Management, vol. 15, no. 1-2, pp. 1-27, 2007.
Burez J. Van den Poel D. Handling class imbalance in customer churn prediction, Expert Systems with Applications 36, 4626–4636, 2009
Califf M. E. Mooney R. J. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction, Journal of Machine Learning Research 4,177-210, 2003.
Cao L. Zhao Y. Zhang C. Mining Impact-Targeted Activity Patternsin Imbalanced Data, IEEE Transactions on knowledge and data engineering, Vol. 20, NO. 8, 2008.
Chawla N. V. Japkowicz N. lcz A. K. Editorial: Special Issue on Learning from Imbalanced Data Sets, Sigkdd Explorations, 6(1):1–6, 2004.
Chen M. C. Chen L. S. Hsu C. C. Zeng W. R. An information granulation based data mining approach for classifying imbalanced data, Information Sciences 178, 3214–3227, 2008.
Clark E. Exploiting stochastic dominance to generate abnormal stock returns, Journal of Financial Markets 20, 20–38, 2014.
Cover T. M. Thomas J. A. Entropy, Relative Entropy and Mutual Information; Elements of Information Theory, ISBN 0-471-06259-6-pp: 12-49, 1991.
Duong T. V. Bui H. H. Phung D. Q. Venkatesh S. Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005.
García V. Sánchez J.S. Mollineda R.A. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowledge-Based Systems 25, 13–21, 2012.
Gong R.S. A Segmentation and Re-balancing Approach for Classification of Imbalanced Data, PHD theses, University of Cincinnati, 2010.
Hoffman M. L. Moral internalization: Current theory and research, In L. Berkowitz (Ed.), Advances in experimental social psychology10, 85-133, 1977.
Hu D. H. Zhang X. X. Yin J. Zheng V. W. Yang Q. Abnormal Activity Recognition Based on HDP-HMM Models, the Twenty-First International Joint Conference on Artificial Intelligence, 2009.
Japkowicz, N., The class imbalance problem: Significance and strategies, the international conference on artificial intelligence: Special track on inductive learning, 2000.
Joshi M. V, Learning Classifier Models for Predicting Rare Phenomena, PhD thesis, University of Minnesota, Twin Cites, Minnesota, USA, 2002.
Kim Y. Sohn S.Y. Stock fraud detection using peer group analysis, Expert Systems with Applications 39, 8986–8992, 2012.
Kou Y, Abnormal Pattern Recognition in Spatial Data, PHD theses, Faculty of Virginia Polytechnic Institute and State University, 2006.
Li X. Rao F. Outlier Detection Using the Information Entropy of Neighborhood Rough Sets, Journal of Information & Computational Science, 3339–3350, 2012.
McCarthy J. Applications of circumscription to formalizing common-sense knowledge,Artificial Intelligence 28, 89-116, 1986.
Nagi J. An intelligent system for detection of non-technical losses in Tanaga National Berhad (TNB) Malaysia low voltage distribution network, PhD Thesis, Tenaga national university,2009.
QamarU.Automated Entropy Value Frequency (AEVF) Algorithm for OutlierDetection in Categorical Data, Recent Advances in Knowledge Engineering and Systems Science,28-35, 2011.
Reiter R. A Theory of Diagnosis from First Principles, Artificial Intelligence 32, 57-95, 1987.
Setyohadi D. B. Abu Bakar A. Othman Z.A. Rough K-means Outlier Factor Based on Entropy Computation, Research Journal of Applied Sciences, Engineering and Technology 8(3): 398-409, 2014.
Weiss G. Mining with rarity: A unifying framework. SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets,6(1):7–19, 2004.
Xiang T. Gong S. Video Behavior Profiling for Anomaly Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(5), 893–908, 2008.
Albanis G. Batchelor R. Combining heterogeneous classifiers for stock selection, Intelligent Systems in Accounting, Finance and Management, vol. 15, no. 1-2, pp. 1-27, 2007.
Burez J. Van den Poel D. Handling class imbalance in customer churn prediction, Expert Systems with Applications 36, 4626–4636, 2009
Califf M. E. Mooney R. J. Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction, Journal of Machine Learning Research 4,177-210, 2003.
Cao L. Zhao Y. Zhang C. Mining Impact-Targeted Activity Patternsin Imbalanced Data, IEEE Transactions on knowledge and data engineering, Vol. 20, NO. 8, 2008.
Chawla N. V. Japkowicz N. lcz A. K. Editorial: Special Issue on Learning from Imbalanced Data Sets, Sigkdd Explorations, 6(1):1–6, 2004.
Chen M. C. Chen L. S. Hsu C. C. Zeng W. R. An information granulation based data mining approach for classifying imbalanced data, Information Sciences 178, 3214–3227, 2008.
Clark E. Exploiting stochastic dominance to generate abnormal stock returns, Journal of Financial Markets 20, 20–38, 2014.
Cover T. M. Thomas J. A. Entropy, Relative Entropy and Mutual Information; Elements of Information Theory, ISBN 0-471-06259-6-pp: 12-49, 1991.
Duong T. V. Bui H. H. Phung D. Q. Venkatesh S. Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005.
García V. Sánchez J.S. Mollineda R.A. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowledge-Based Systems 25, 13–21, 2012.
Gong R.S. A Segmentation and Re-balancing Approach for Classification of Imbalanced Data, PHD theses, University of Cincinnati, 2010.
Hoffman M. L. Moral internalization: Current theory and research, In L. Berkowitz (Ed.), Advances in experimental social psychology10, 85-133, 1977.
Hu D. H. Zhang X. X. Yin J. Zheng V. W. Yang Q. Abnormal Activity Recognition Based on HDP-HMM Models, the Twenty-First International Joint Conference on Artificial Intelligence, 2009.
Japkowicz, N., The class imbalance problem: Significance and strategies, the international conference on artificial intelligence: Special track on inductive learning, 2000.
Joshi M. V, Learning Classifier Models for Predicting Rare Phenomena, PhD thesis, University of Minnesota, Twin Cites, Minnesota, USA, 2002.
Kim Y. Sohn S.Y. Stock fraud detection using peer group analysis, Expert Systems with Applications 39, 8986–8992, 2012.
Kou Y, Abnormal Pattern Recognition in Spatial Data, PHD theses, Faculty of Virginia Polytechnic Institute and State University, 2006.
Li X. Rao F. Outlier Detection Using the Information Entropy of Neighborhood Rough Sets, Journal of Information & Computational Science, 3339–3350, 2012.
McCarthy J. Applications of circumscription to formalizing common-sense knowledge,Artificial Intelligence 28, 89-116, 1986.
Nagi J. An intelligent system for detection of non-technical losses in Tanaga National Berhad (TNB) Malaysia low voltage distribution network, PhD Thesis, Tenaga national university,2009.
QamarU.Automated Entropy Value Frequency (AEVF) Algorithm for OutlierDetection in Categorical Data, Recent Advances in Knowledge Engineering and Systems Science,28-35, 2011.
Reiter R. A Theory of Diagnosis from First Principles, Artificial Intelligence 32, 57-95, 1987.
Setyohadi D. B. Abu Bakar A. Othman Z.A. Rough K-means Outlier Factor Based on Entropy Computation, Research Journal of Applied Sciences, Engineering and Technology 8(3): 398-409, 2014.
Weiss G. Mining with rarity: A unifying framework. SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets,6(1):7–19, 2004.
Xiang T. Gong S. Video Behavior Profiling for Anomaly Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(5), 893–908, 2008.
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