Optimizing shapelets quality measure for imbalanced time series classification

Springer Science and Business Media LLC - Tập 50 Số 2 - Trang 519-536 - 2020
Qiuyan Yan1, Yang Cao1
1Computer Science and Technology, China University of Mining Technology, Xuzhou, China

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Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Progress Artif Intell 5(4):1–12

Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying: mining of time series data experimental comparison of representations and distance measures. Proc VLDB Endow 1(2):1542–1552

Ye L, Keogh E (2011) Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Mining Knowl Discov 22(1–2):149–182

Lin J, Keogh E, Li W, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. Data Mining Knowl Discov 15(2):107–144

Lines J, Davis LM, Hills J, Bagnall A (2012) A shapelet transform for time series classification. In: Acm Sigkdd international conference on knowledge discovery & data mining

Yan Q, Sun Q, Yan X (2016) Adapting ELM to time series classification: a novel diversified top-k shapelets extraction method. In: Databases theory and applications - 27th Australasian database conference, ADC, pp 215–227

Deng H, Runger G, Tuv E, Vladimir M (2013) A time series forest for classification and feature extraction. Inf Sci 239(4):142– 153

Mohan S, Zhihai W (2018) Random Pairwise shapelets forest[C]. In: Advances in knowledge discovery and data mining, pp 68–80

Collell G, Prelec D, Patil KR (2018) A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data[J]. Neurocomputing 275:330–340

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357

Han H, Wang W, Mao B (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Proceedings of advances in intelligent computing, pp 878–887

Nitesh V, Chawla L (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: Knowledge discovery in databases: PKDD 2003, pp 107–119

Zhou C, Liu B, Wang S (2016) CMO-SMOTE: misclassification cost minimization oriented synthetic minority oversampling technique for imbalanced learning. In: International conference on intelligent human-machine systems & cybernetics

He H, Yang B, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International joint conference on neural networks, pp 1322–1328

Bo T, He H (2017) GIR-based ensemble sampling approaches for imbalanced learning. Pattern Recogn 71:306–319

Zhang C, Guo J, Qi C, Jiang ZL, Xuan W (2018) EHSBoost: enhancing ensembles for imbalanced data-sets by evolutionary hybrid-sampling. In: International conference on security, pattern analysis, and cybernetics (SPAC)

Braytee A, Hussain FK, Anaissi A, Kennedy PJ (2015) ABC-sampling for balancing imbalanced datasets based on artificial bee colony algorithm. In: 2015 IEEE 14th international conference on machine learning and applications (ICMLA), pp 594–599

Kang Q, Chen X, Li S, Zhou M (2017) A noise-filtered under-sampling scheme for imbalanced classification. IEEE Trans Cybern 47(12):4263–4274

Rivera WA (2017) Noise reduction a priori synthetic over-sampling for class imbalanced data sets. Inf Sci 408:146–161

Zhang W, Kobeissi S, Tomko S, Challis C (2017) Adaptive sampling scheme for learning in severely imbalanced large scale data. In: Proceedings of the Ninth Asian conference on machine learning, pp 240–247

Zhu T, Lin Y, Liu Y (2017) Synthetic minority oversampling technique for multiclass imbalance problems. Pattern Recogn 72:327–340

Alejo R, Monroy-De-Jesús J, Ambriz-Polo JC, Pacheco-Sánchez JH (2017) An improved dynamic sampling back-propagation algorithm based on mean square error to face the multi-class imbalance problem[J]. Neural Comput Appl 1:1–15

García-Pedrajas N, Romero Del Castillo JA, Cerruela-García G (2017) A proposal for local k values for k-nearest neighbor rule. IEEE Trans Neural Netw Learn Syst 28(2):470–475

Mullick SS, Datta S, Das S (2018) Adaptive learning-based k-nearest neighbor classifiers with resilience to class imbalance. IEEE Trans Neural Netw Learn Syst 29(11):5713–5725

Deepak G, Bharat R (2018) Entropy based fuzzy least squares twin support vector machine for class imbalance learning. Appl Intell 48(11):4212–4231

Xu Y, Wang Q (2018) Maximum margin of twin spheres machine with pinball loss for imbalanced data classification. Appl Intell 48(1):23–34. learning. Applied intelligence, 1–20

Lines J, Taylor S, Bagnall AJ (2018) Time Series Classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles. TKDD 12(5):51–52

Chen Z, Lin T (2018) A synthetic neighborhood generation based ensemble learning for the imbalanced data classification. Appl Intell 48(8):2441–2457

Cao H, Li X-L, Woon Y-K, Ng S-K (2011) SPO: structure preserving oversampling for imbalanced time series classification. In: IEEE 11th international conference on data mining

Cao H, Li XLi, Woon YK, Ng SK (2013) Integrated oversampling for imbalanced time series classification. IEEE Trans Knowl Data Eng 25(12):2809–2822

Liang G, Zhang C (2012) An efficient and simple under-sampling technique for imbalanced time series classification. In: Acm International conference on information & knowledge management

Liang G (2013) An effective method for imbalanced time series classification: hybrid sampling. In: Proceedings of the 26th Australasian joint conference on ai 2013: advances in artificial intelligence, pp 374–385

Gong Z, Chen H (2016) Model-based oversampling for imbalanced sequence classification. In: CIKM, pp 1009–1018

Ye L, Keogh EJ (2009) Time series shapelets: a new primitive for data mining. In: Acm Sigkdd international conference on knowledge discovery & data mining, pp 947–956

He Q, Zhidong, Zhuang F , Shang T, Shi Z (2012) Fast time series classification based on infrequent shapelets. In: International conference on machine learning & applications, pp 215–219

Zakaria J, Mueen A, Keogh E (2012) Clustering time series using unsupervised-shapelets. In: IEEE International conference on data mining, pp 785–794

Dong YJ, Hai WZ, Meng H (2015) Shapelet pruning and shapelet coverage for time series classification. J Softw, 2311–2325

Mueen A, Keogh E, Young N (2011) Logical-shapelets: an expressive primitive for time series classification. in: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1154–1162

Hou L, Kwok JT, Zurada JM (2016) Efficient learning of timeseries shapelets. In: Thirtieth Aaai conference on artificial intelligence, pp 1209–1215

The UCR Time Series Classification Archive. (2015) www.cs.ucr.edu/eamonn/time_series_data/

Cao H, Tan, et al (2014) A parsimonious mixture of Gaussian trees model for oversampling in imbalanced and multimodal time-series classification. IEEE Trans Neural Netw Learn Syst 25(12):2226–2239

Keerthi SS, Shevade SK, Bhattacharyya C, et al (2001) Improvements to Platt’s SMO algorithm for SVM classifier design. Neur Comput 13(3):637–649