候選標(biāo)記信息感知的偏標(biāo)記學(xué)習(xí)算法
doi: 10.11999/JEIT181059
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國家數(shù)字交換系統(tǒng)工程技術(shù)研究中心 ??鄭州 ??450002
基金項(xiàng)目: 國家自然科學(xué)基金(61601513)
Candidate Label-Aware Partial Label Learning Algorithm
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National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, China
Funds: The National Natural Science Foundation of China (61601513)
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摘要: 在偏標(biāo)記學(xué)習(xí)中，示例的真實(shí)標(biāo)記隱藏在由一組候選標(biāo)記組成的標(biāo)記集中。現(xiàn)有的偏標(biāo)記學(xué)習(xí)算法在衡量示例之間的相似度時(shí)，只基于示例的特征進(jìn)行計(jì)算，缺乏對(duì)候選標(biāo)記集信息的利用。該文提出一種候選標(biāo)記感知的偏標(biāo)記學(xué)習(xí)算法(CLAPLL)，在構(gòu)建圖的階段有效地結(jié)合候選標(biāo)記集信息來衡量示例之間的相似度。首先，基于杰卡德距離和線性重構(gòu)，計(jì)算出各個(gè)示例的標(biāo)記集之間的相似度，然后結(jié)合示例相似度和標(biāo)記集的相似度構(gòu)建相似度圖，并通過現(xiàn)有的基于圖的偏標(biāo)記學(xué)習(xí)算法進(jìn)行學(xué)習(xí)和預(yù)測(cè)。3個(gè)合成數(shù)據(jù)集和6個(gè)真實(shí)數(shù)據(jù)集上實(shí)驗(yàn)結(jié)果表明，該文方法相比于基線算法消歧準(zhǔn)確率提升了0.3%～16.5%，分類準(zhǔn)確率提升了0.2%～2.8%。
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關(guān)鍵詞:
- 偏標(biāo)記學(xué)習(xí) /
- 弱監(jiān)督學(xué)習(xí) /
- 消歧 /
- 杰卡德距離 /
- 線性重構(gòu)
Abstract: In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels. The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information. In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase. First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction. The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3%～16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2%～2.8%. -
表 1 候選標(biāo)記信息感知的偏標(biāo)記學(xué)習(xí)算法偽代碼
輸入：偏標(biāo)記數(shù)據(jù)集$D = \left\{ {({X_i},{S_i})|1 \le i \le m} \right\}$，最近鄰樣本數(shù) $k$，標(biāo)記相似度權(quán)重$\alpha $ 訓(xùn)練階段： 1 對(duì)特征矩陣${\text{X}} \in {{\text{R}}^{m \times d}}$進(jìn)行Z-score歸一化； 2 根據(jù)式(1)求${{\text{w}}_j}$； 3 根據(jù)${{\text{w}} _j}$構(gòu)建相似度圖${G_i}(V,E)$； 4 switch v； case Jaccard：根據(jù)式(3)計(jì)算${{\text{u}}_j}$，并構(gòu)建候選標(biāo)記集相似度 圖${G_{\rm{c}}}(i,j)$, (CAP-J算法)； case linear：根據(jù)式(4)計(jì)算${{\text{u}}_j}$，并構(gòu)建候選標(biāo)記集相似度 圖${G_{\rm{c}}}(i,j)$, (CAP-L算法)； end switch 5 根據(jù)式(7)計(jì)算最終相似度圖$G(i,j)$； 6 結(jié)合現(xiàn)有圖模型偏標(biāo)記學(xué)習(xí)算法進(jìn)行消歧，得到消歧結(jié)果 $\mathop D\limits^ \wedge = \left\{ {({X_i},{{\widehat y}_i})|1 \le i \le m} \right\}$； 測(cè)試階段： 7 對(duì)于未見示例${x^*}$，根據(jù)式(8)計(jì)算得分類結(jié)果； 輸出：消歧結(jié)果$\mathop D\limits^ \wedge = \left\{ {({X_i},{{\widehat y}_i})|1 \le i \le m} \right\}$和分類結(jié)果${y^*}$。 
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表 2 基線算法和本文算法復(fù)雜度比較
算法復(fù)雜度 實(shí)際復(fù)雜度 基線算法 $O({d^{\,\; 2} }{n^3}\lg (n))$ $O({d^{\,\; 2} }{n^3}\lg (n))$ 本文算法(CAP-J) $O({d^{\,\; 2} }{n^3}\lg (n) + (s + 1){k^2})$ $O({d^{\,\; 2} }{n^3}\lg (n))$ 本文算法(CAP-L) $O({d^{\,\; 2} }{n^3}\lg (n) + (sk + 1){k^2})$ $O({d^{\,\; 2} }{n^3}\lg (n))$
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表 3 真實(shí)偏標(biāo)記數(shù)據(jù)集的特征
數(shù)據(jù)集 樣本數(shù) 特征數(shù) 類別標(biāo)記數(shù) 候選標(biāo)記數(shù) 平均 最小 最大 Lost 1122 108 16 2.23 1 3 Birdsong 4998 38 13 2.18 1 4 MSRSCv2 1758 48 23 3.16 1 7 FG-NET 1002 262 78 7.48 2 11 Yahoo! News 22991 163 219 1.91 1 5 Soccer Player 17472 279 171 2.09 1 11
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表 4 合成偏標(biāo)記數(shù)據(jù)集的特征
數(shù)據(jù)集 樣本數(shù) 特征數(shù) 類別標(biāo)記數(shù) 參數(shù)設(shè)置 Ecoli 336 7 8 p={0.1, 0.2, 0.3, 0.4,0.5, 0.6, 0.7, 0.8} r={1, 2, 3, 4, 5} Movement 360 90 15 CTG 2126 21 10
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表 5 不同算法在真實(shí)偏標(biāo)記數(shù)據(jù)集上的消歧準(zhǔn)確率(%)
數(shù)據(jù)集 消歧準(zhǔn)確率(mean±std.) Lost MSRCv2 BirdSong FG-NET Soccer Player Yahoo! News PLKNN 67.54±0.09 51.00±0.09 68.69±0.04 11.06±0.13 52.60±0.02 66.06±0.02 CAP-JKNN 73.60±0.10 62.19±0.08 77.14±0.04 14.71±0.15 69.55±0.01 80.00±0.02 CAP-LKNN 73.38±0.13 61.88±0.09 76.67±0.04 14.81±0.17 69.22±0.02 79.78±0.05 PLKNN(監(jiān)督) 84.93±0.04 73.07±0.02 84.29±0.14 14.94±0.05 90.65±0.03 91.21±0.03 IPAL 84.01±0.15 70.58±0.15 83.61±0.04 15.28±0.19 67.65±0.03 84.99±0.05 CAP-JIPAL 85.58±0.17 71.25±0.20 84.22±0.04 15.40±0.19 67.94±0.02 85.33±0.04 CAP-LIPAL 85.39±0.24 70.92±0.12 84.40±0.05 14.86±0.17 67.89±0.07 85.21±0.03 IPAL(監(jiān)督) 85.43±0.32 76.43±0.22 85.92±0.10 15.53±0.18 71.43±0.05 86.43±0.06 LALO 75.05±1.24 59.42±0.89 78.14±0.75 15.92±0.69 – – CAP-JLALO 76.80±1.11 59.48±1.09 78.02±0.81 15.69±0.75 – – CAP-LLALO 80.22±1.08 59.72±0.82 78.24±0.64 15.76±0.94 – – LALO(監(jiān)督) 84.53±1.53 60.04±1.14 79.25±0.88 16.13±0.62 – –
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表 6 不同算法在真實(shí)偏標(biāo)記數(shù)據(jù)集上的分類準(zhǔn)確率(%)
數(shù)據(jù)集 消歧準(zhǔn)確率(mean±std.) Lost MSRCv2 BirdSong FG-NET Soccer Player Yahoo! News PLKNN 61.48±0.78 44.12±0.36 64.66±0.23 5.58±0.42 49.55±0.04 58.30±0.06 CAP-JKNN 64.01±0.65 46.35±0.38 66.01±0.26 6.24±0.38 50.77±0.09 61.18±0.05 CAP-LKNN 63.58±0.72 46.14±0.48 65.88±0.21 5.74±0.56 50.43±0.09 60.50±0.12 PLKNN(監(jiān)督) 69.26±0.48 51.33±0.30 68.49±0.13 6.98±0.21 54.26±0.05 61.53±0.08 IPAL 73.18±0.79 53.08±0.33 71.09±0.33 5.28±0.55 54.84±0.10 65.88±0.14 CAP-JIPAL 73.95±0.68 53.35±0.50 71.34±0.30 5.45±0.60 55.00±0.10 66.02±0.16 CAP-LIPAL 73.44±0.68 52.61±0.71 71.60±0.26 5.89±0.57 54.46±0.18 66.02±0.18 IPAL (監(jiān)督) 75.04±0.82 55.71±0.46 72.05±0.27 5.95±0.62 55.38±0.13 66.83±0.15 LALO 72.15±3.04 50.13±2.03 72.99±1.54 6.11±1.61 – – CAP-JLALO 73.02±2.88 49.23±2.10 73.00±1.62 5.96±1.19 – – CAP-LLALO 74.84±2.20 50.27±3.19 73.37±1.50 6.76±1.64 – – LALO(監(jiān)督) 76.68±2.19 52.31±2.49 74.87±1.26 7.03±1.29 – –
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