基于BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)偽最近鄰分類

曾勇; 舒歡; 胡江平; 葛月月

doi:10.11999/JEIT160133

基于BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)偽最近鄰分類

doi: 10.11999/JEIT160133

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61104104, 61473061)，四川省信號(hào)與信息重點(diǎn)實(shí)驗(yàn)室基金(SZJJ2009-002)

計(jì)量
- 文章訪問(wèn)數(shù): 1138
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-01-29
- 修回日期: 2016-06-17
- 刊出日期: 2016-11-19

Adaptive Pseudo Nearest Neighbor Classification Based on BP Neural Network

Funds:

The National Natural Science Foundation of China (61104104, 61473061), The Fund of Sichuan Provincial Key Laboratory of Signal and Information Processing (SZJJ2009-002)

摘要

摘要: 在偽最近鄰(PNN)分類算法中，待分類樣本點(diǎn)與每一類樣本集中各個(gè)近鄰的距離加權(quán)系數(shù)都是主觀確定的，這就使得算法得不到最優(yōu)距離加權(quán)值。針對(duì)這一問(wèn)題，該文提出一種基于BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)偽最近鄰分類算法。首先通過(guò)計(jì)算待分類樣本點(diǎn)與每一類樣本集中各個(gè)近鄰的距離值，并將其作為BP神經(jīng)網(wǎng)絡(luò)的輸入。然后根據(jù)BP神經(jīng)網(wǎng)絡(luò)輸入與輸出之間的映射來(lái)自適應(yīng)確定相應(yīng)的距離加權(quán)值。最后由BP神經(jīng)網(wǎng)絡(luò)的輸出值判別樣本類別號(hào)。實(shí)驗(yàn)結(jié)果表明，該算法能夠自適應(yīng)地調(diào)節(jié)距離加權(quán)系數(shù)，同時(shí)還能有效地改善分類準(zhǔn)確率。
- 偽最近鄰分類 /
- BP神經(jīng)網(wǎng)絡(luò) /
- 自適應(yīng)
Abstract: Distance-weighted coefficients between unlabeled sample point and its nearest neighbors belonging to same sample set are determined subjectively in the Pseudo Nearest Neighbor (PNN) classification algorithm, which makes it difficult to obtain optimal distance-weighted value. In this paper, an adaptive pseudo neighbor classification algorithm based on BP neural network is proposed. Firstly, the distance-weighted values between unlabeled sample point and its neighbors lying in the same sample set are regarded as the input of BP neural network. Secondly, the corresponding distance-weighted values are adaptively determined according to the mapping between the inputs and outputs of BP neural network. Finally, the classification of unlabeled sample point is judged by the outputs of BP neural network. Experimental results show that the proposed approach adaptively adjusts the distance-weighted coefficients. Moreover, the classification accuracy can be effectively improved.
- Pseudo Nearest Neighbor (PNN) classification /
- BP neural network /
- Adaptive

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