K插值單純形法核極限學(xué)習(xí)機的研究

蘇一丹; 李若愚; 覃華; 陳琴

doi:10.11999/JEIT171104

K插值單純形法核極限學(xué)習(xí)機的研究

doi: 10.11999/JEIT171104 cstr: 32379.14.JEIT171104

廣西大學(xué)計算機與電子信息學(xué)院南寧 530004

基金項目: 國家自然科學(xué)基金(61762009)

詳細信息

作者簡介:
蘇一丹：男，1962年生，教授，研究方向為自然計算、數(shù)據(jù)挖掘

李若愚：男，1989年生，碩士生，研究方向為智能優(yōu)化及在數(shù)據(jù)挖掘中的應(yīng)用

覃華：男，1972年生，教授，研究方向為量子計算理論與近似動態(tài)規(guī)劃最優(yōu)化方法、數(shù)據(jù)挖掘

陳琴：女，1974年生，副教授，研究方向為數(shù)據(jù)挖掘

通訊作者:
覃華 1694520545@qq.com

中圖分類號: TP181
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- 文章訪問數(shù): 1770
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-11-24
- 修回日期: 2018-04-23
- 網(wǎng)絡(luò)出版日期: 2018-06-07
- 刊出日期: 2018-08-01

Kernel Extreme Learning Machine Based on K Interpolation Simplex Method

College of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Funds: The National Natural Science Foundation of China (61762009)

摘要

摘要: 針對核極限學(xué)習(xí)機高斯核函數(shù)參數(shù)選優(yōu)難，影響學(xué)習(xí)機訓(xùn)練收斂速度和分類精度的問題，該文提出一種K插值單純形法的核極限學(xué)習(xí)機算法。把核極限學(xué)習(xí)機的訓(xùn)練看作一個無約束優(yōu)化問題，在訓(xùn)練迭代過程中，用Nelder-Mead單純形法搜索高斯核函數(shù)的最優(yōu)核參數(shù)，提高所提算法的分類精度。引入K插值為Nelder-Mead單純形法提供合適的初值，減少單純形法的迭代次數(shù)，提高了新算法的訓(xùn)練收斂效率。通過在UCI數(shù)據(jù)集上的仿真實驗并與其它算法比較，新算法具有更快的收斂速度和更高的分類精度。
- 核極限學(xué)習(xí)機 /
- 核參數(shù) /
- Nelder-Mead單純形法 /
- K插值法
Abstract: The kernel Extreme Learning Machine (ELM) has a problem that the kernel parameter of the Gauss kernel function is hard to be optimized. As a result, training speed and classification accuracy of kernel ELM are negatively affected. To deal with that problem, a novel kernel ELM based on K interpolation simplex method is proposed. The training process of kernel ELM is considered as an unconstrained optimal problem. Then, the Nelder-Mead Simplex Method (NMSM) is used as an optimal method to search the optimized kernel parameter, which improves the classification accuracy of kernel ELM. Furthermore, the K interpolation method is used to provide appropriate initial values for the Nelder-Mead simplex to reduce the number of iterations, and as a result, the training speed of ELM is improved. Comparative results on UCI dataset demonstrate that the novel ELM algorithm has better training speed and higher classification accuracy.
- Kernel Extreme Learning Machine (KELM) /
- Kernel parameter /
- Nelder-Mead Simplex Method (NMSM) /
- K Interpolation method

HTML全文

圖 1 高斯核參數(shù)取值對訓(xùn)練精度和測試精度的影響

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圖 2 K插值法對單純形核ELM訓(xùn)練收斂的影響

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圖 3 本文算法的時間復(fù)雜度特征曲線

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表 1 1維單純形子算法

輸入：初始搜索點 \small${\delta _0}$，數(shù)據(jù)集X，目標(biāo)函數(shù)的計算公式 \small$F(\delta )$，最大迭代次數(shù)MT，單純形的最小半徑SR。
輸出：最優(yōu)核參數(shù) \small${\delta ^*}$。
步驟1?根據(jù)給定的初始化點 \small${\delta _0}$構(gòu)造初始單純形；
步驟2?對于1維單純形的兩個頂點A和B，計算出目標(biāo)函數(shù)值 \small$F(\delta _{\rm A})$和 \small$F(\delta _{\rm B})$，函數(shù)值較小的為best點，較大的為worst點；
步驟3?計算反射點R和反射點處的目標(biāo)函數(shù)值 \small$F(\delta _{\rm R})$，如果反射點的目標(biāo)函數(shù)值優(yōu)于best點，則進入步驟4；如果差于best點，則轉(zhuǎn)步驟5；如果反射點與best點處目標(biāo)值相等，則轉(zhuǎn)步驟6；
步驟4?計算擴展點E和擴展點處的目標(biāo)函數(shù)值 \small$F(\delta _{\rm E})$，比較反射點R處和擴展點E的目標(biāo)函數(shù)值，選擇目標(biāo)函數(shù)值較小的點替換單純形中的 worst點，然后轉(zhuǎn)步驟7；
步驟5?比較反射點R與worst點處的目標(biāo)函數(shù)值，根據(jù)比較結(jié)果確定壓縮點M的位置，若壓縮點M的目標(biāo)函數(shù)值 \small$F({\delta _M})$與worst點相比更小，則用M點替換worst點，然后轉(zhuǎn)步驟7；否則，轉(zhuǎn)步驟6；
步驟6?執(zhí)行單純形收縮操作；
步驟7?若達到最大迭代次數(shù)MT，或單純形半徑小于SR，則迭代結(jié)束，best頂點對應(yīng)的 \small$\delta $值即為最優(yōu)核參數(shù)值 \small${\delta ^*}$；否則，轉(zhuǎn)步驟2進行下一次迭代。

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表 2 K插值單純形法核極限學(xué)習(xí)機訓(xùn)練算法

輸入：訓(xùn)練數(shù)據(jù)集X，測試數(shù)據(jù)集Y。
輸出：最優(yōu)核參數(shù) \small${\delta ^*}$、分類函數(shù) \small$\tilde {{f}}(\cdot)$，測試數(shù)據(jù)集的分類結(jié)果。
步驟1?初始化參數(shù)，給定插值數(shù)目K，正則化參數(shù)C，最大迭代次數(shù)MT，單純形最小半徑SR；
步驟2?數(shù)據(jù)預(yù)處理；
步驟3?計算K個插值核參數(shù)在訓(xùn)練數(shù)據(jù)集X上的訓(xùn)練精度，并從中找到合適的初始搜索點 \small${\delta _0}$：
(1)構(gòu)造核參數(shù) \small$\delta $的K個插值點 \small${\delta _i}$。 \small${\delta _i} = {2^{i - 1}}$，其中 \small$i = 1,2, ·\!·\!· ,K$；
(2)用式(8)分別計算K個插值點 \small${\delta _i}$下相應(yīng)的分類參數(shù) \small${\tilde {β} _i}$，其中i=1,2,···,K；
(3)用式(9)計算K個插值點 \small${\delta _i}$下的分類結(jié)果和訓(xùn)練精度 \small${P_{{\delta _i}}}$，其中i=1,2,···,K； (4)構(gòu)造訓(xùn)練精度矩陣 \small${S} = \left[ {\begin{array}{*{20}{c}} {{\delta _1}}&{{P_{{\delta _1}}}} \\ {{\delta _2}}&{{P_{{\delta _2}}}} \\ \vdots & \vdots \\ {{\delta _k}}&{{P_{\delta_k}}} \end{array}} \right]$；
(5)刪除訓(xùn)練精度矩陣 \small${S}$中 \small${P_{{\delta _i}}} \ge 0.999$的行，得到 \small${S}'$；
(6)取 \small${S}'$矩陣中的 \small${S}'\left( {1,1} \right)$元素作為初始搜索點 \small${\delta _0}$；
步驟4?調(diào)用單純形法優(yōu)化核參數(shù)子算法，進行迭代搜索，傳入搜索初值 \small${\delta _0}$，數(shù)據(jù)集X，目標(biāo)函數(shù)計算公式 \small$F(\delta )$，最大迭代次數(shù)MT和單純形最小半徑SR，得到最優(yōu)核參數(shù) \small${\delta ^*}$；
步驟5?根據(jù)最優(yōu)核參數(shù) \small${\delta ^*}$，用式(8)計算核ELM的分類參數(shù) \small$\tilde {β} $；
步驟6?對于測試數(shù)據(jù)集Y中的待分類樣本 \small${{x}_p}$，根據(jù)最優(yōu)核參數(shù)和步驟5的 \small$\tilde{β} $，用式(9)計算 \small${{x}_p}$的類別。

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表 3 實驗數(shù)據(jù)集列表

數(shù)據(jù)集	樣本數(shù)目	屬性數(shù)目	簇的數(shù)目	數(shù)據(jù)集	樣本數(shù)目	屬性數(shù)目	簇的數(shù)目
DNA	1967	180	3	Segment	2310	19	7
Letter	20000	16	26	Satellite	6435	36	6
Msplice	3044	240	3	Vowel	990	14	11
Musk	5692	166	2	Liver	345	7	2
Cnae	1080	857	9	Wilt	4839	5	2
Chess	28056	6	18	D31	3100	2	31

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表 4 兩種核極限學(xué)習(xí)機算法的分類精度比較

數(shù)據(jù)集	傳統(tǒng)核ELM(%)									KS-KELM
數(shù)據(jù)集	\small$\delta $=0.01	\small$\delta $=0.1	\small$\delta $=1	\small$\delta $=10	\small$\delta $=50	\small$\delta $=100	最好精度	最差精度	平均精度	\small$\delta $值	精度(%)
DNA	21.41	24.63	80.94	94.65	66.38	55.03	94.65	21.41	57.17	4.5	95.72
Letter	4.19	83.32	89.04	77.88	54.22	49.69	89.04	4.19	59.72	2.4521	96.92
Msplice	54.86	59.59	79.53	94.24	75.52	54.86	94.24	54.86	69.77	6	94.82
Musk	84.08	86.09	89.96	95.22	95.94	96.13	96.13	84.08	91.24	224	96.99
Cnae	9.27	80.39	91.38	88.58	71.55	49.35	91.38	9.27	65.09	1.6631	92.67
Chess	0.17	58.55	62.10	32.82	24.35	22.08	62.10	0.17	33.35	0.832	63.21
D31	89.33	95.67	96.67	92.33	23.50	16.33	96.67	16.33	68.97	1.7471	97.17

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表 5 5種極限學(xué)習(xí)機算法分類精度的比較

數(shù)據(jù)集	WMOS-2017^[19]	M-2016^[20]	IC-2017^[21]	H-2016^[22]	KS-KELM
Segment	94.12	95.56	—	—	95.12
Satellite	88.54	88.25	80.03	90.9	91.30
Letter	86.48	72.30	76.84	95.82	96.92
Vowel	88.78	59.74	—	64.94	92.12
Liver	—	—	66.56	72.17	80.26
Wilt	96.18	97.35	96.52	—	98.06

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