基于ROC的三元再編碼研究

雷蕾; 王曉丹; 羅璽

doi:10.11999/JEIT151343

基于ROC的三元再編碼研究

doi: 10.11999/JEIT151343

1.
(空軍工程大學防空反導學院西安 710051) ②(空軍工程大學信息與導航學院西安 710077)

基金項目:

國家自然科學基金(61273275, 61503407)

計量
- 文章訪問數(shù): 1186
- HTML全文瀏覽量: 95
- PDF下載量: 340
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-12-01
- 修回日期: 2016-06-06
- 刊出日期: 2016-10-19

Recoding Error-correcting Output Codes Based on Receiver Operating Characteristics

1.
(Institute of Air and Missile Defense, Aire Force Engineering University, Xi&rsquo
2.
(Institute of Information and Navigation, Aire Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61273275, 61503407)

摘要

摘要: 針對三元編碼矩陣中基分類器不包含被忽略樣本類別先驗知識的問題，該文提出一種基于接收機工作特性(ROC)曲線的矩陣再編碼方法。首先基于ROC曲線尋找構造拒絕域的閾值對，從而獲得最優(yōu)分類器；然后利用最優(yōu)分類器對訓練樣本中被忽略的類別進行分類，將經典的二值輸出變?yōu)槿递敵?，從而對初始編碼矩陣的碼元0進行重新編碼。在解碼階段，采用經典的漢明距離解碼方法對未知樣本進行決策。該方法能夠避免基分類器的二次訓練，適用于任意的三元糾錯輸出編碼，具有良好的普適性和實用性。基于人工和UCI公共數(shù)據(jù)集的實驗結果表明該方法簡單高效，在不增加訓練時間的基礎上，能夠提高解碼的速度和精度，促進分類效果的提升。
- 三元糾錯輸出編碼 /
- 二次編碼 /
- 最優(yōu)分類器 /
- 拒絕域 /
- 接收機工作特性
Abstract: As to the problem that the base classifiers in ternary Error Correcting Output Codes (ECOC) matrix do not contain the prior information of classes which are ignored in binary splits, a new recoding ECOC based on Receiver Operating Characteristic (ROC) curve is presented. To recode the ternary matrix, the two thresholds of reject region are obtained based on ROC to build the optimal classifiers. Then, the optimal classifiers are used to classify the ignored classes based on bipartition in training phase. In so doing, the classical two-symbol output expands to three-symbol to recode the zeros. Finally, the Hamming decoding strategy is adopted for decision in decoding. This method can avoid a second training and is applied to any kind of ternary matrix. The experiments based on Synthetic and UCI datasets validate the better efficiency and remarkable promotion without increasing training complexity of the proposed approach.
- Ternary error-correcting output codes /
- Recoding /
- Optimal classifier /
- Reject option /
- Receiver Operating Characteristics (ROC)

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參考文獻(20)

DIETTERICH T G and BAKIRI G. Solving multi-class learning problems via error-correcting output codes[J]. Journal of Artificial Intelligence Research, 1995, 34(2): 263-286. doi: 10.1613/jair.105.

PHYO K S, JIAN G W, and EAM K T. Facial age range estimation with extreme learning machines[J]. Neurocomputing, 2015, 149A: 364-372. doi: 10.1016/ j.neucom.2014.03.074.

ELIF D. ECG beats classification using multiclass support vector machines with error correcting output codes[J]. Digital Signal Processing, 2007, 45(17): 675-684. doi: 10.1016/j.dsp. 2006.11.009.

SERGIO E, DAVID M, ELOI P, et al. Online error correcting output codes[J]. Pattern Recognition Letters, 2011, 32(3): 458-467. doi: 10.1016/j.patrec.2010.11.005.

ERIN L A, ROBERT E S, YORAM S, et al. Reducing multiclass to binary: a unifying approach for margin classifiers[J]. Journal of Machine Learning Research, 2000, 39(1): 113-141. doi: 10.1162/15324430152733133.

SERGIO E, ORIOL P, and PETIA R. Separability of ternary error-correcting output codes[J]. Pattern Recognition Letters, 2009, 30(5): 285-297. doi: 10.1016/j.patrec.2008.10.002.

SERGIO E, DAVID M J T, ORIOL P, et al. Subclass problem-dependent design for error-correcting output codes [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(6): 1041-1054. doi: 10.1109/TPAMI. 2008.38.

周進登, 王曉丹, 周紅健. 基于混淆矩陣的自適應糾錯輸出編碼多類分類方法[J].系統(tǒng)工程與電子技術, 2012, 34(7): 220-226. doi: 10.3969/j.issn.1001-506X.2012.07.38.

ZHOU Jindeng, WANG Xiaodan, and ZHOU Hongjian. Multiclass classification of adaptive error-correcting output codes based on confusion matrix[J]. Systems Engineering and Electronics, 2012, 34(7): 220-226. doi: 10.3969/j.issn. 1001-506X.2012.07.38.

WANG Y, CHEN S C, and XUE H. Can under-exploited structure of original-classes help ECOC-based multi-class classification?[J]. Neurocomputing, 2012, 89: 158-167. doi: 10.1016/j.neucom.2012.02.035.

SERGIO E, ORIOL P, and PETIA R. Re-coding ECOCs without Re-training[J]. Pattern Recognition Letters, 2010, 31(7): 555-562. doi: 10.1016/j.patrec.2009.12.002.

MIGUEL A B, SERGIO E, XAVIER B, et al. On the design of an ECOC-compliant genetic algorithm[J]. Pattern Recognition, 2014, 47(2): 865-884. doi: 10.1016/j.patcog. 2013.06.019.

FRANCESCO C, ORIOL P, and PETIA R. ECOC-DRF: discriminative random fields based on error correcting output codes[J]. Pattern Recognition, 2014, 47(6): 2193-2204. doi: 10.1016/j.patcog.2013.12.007.

MIKEL G, ALBERTO F, EDURNE B, et al. DRCW-OVO: distance-based relative competence weighting combination for one-vs-one strategy in multi-class problems[J]. Pattern Recognition, 2015, 48(1): 28-52. doi: 10.1016/j.patcog. 2014.07.023.

LEI L, WANG X D, LUO X, et al. Hierarchical error-correcting output codes based on SVDD[J]. Pattern Analysis and Applications, 19(1): 163-171. doi: 10.1007/ s10044-015-0455-5.

TADEUSZ P. On the use of ROC analysis for the optimization of abstaining classifiers[J]. Machine Learning, 2007, 68(2): 137-169. doi: 10.1007/s10994-007-5013-y.

ZHOU J D, and WANG X D. Research on the unbiased probability estimation of error-correcting output coding[J]. Pattern Recognition, 2011, 44(7): 1552-1565. doi: 10.1016/ j.patcog.2010.12.020.

ZHOU J D, YUN Y，ZHANG J M, et al. Constructing ECOC based on confusion matrix for multiclass learning problems[J]. Science China Information Sciences, 2016, 59(1): 1-14. doi: 10.10071/s11432-015-5321-y.

鄒洪俠, 秦峰. 二類分類器的ROC曲線生成算法[J]. 計算機技術與發(fā)展, 2009, 19(6): 109-112.

ZOU Hongxia and QIN Feng, Algorithm for generating ROC curve of two-classifier[J]. Computer Technology and Development, 2009, 19(6): 109-112.

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