本文選題：光譜分類 + Adaboost��； 參考：《光譜學(xué)與光譜分析》2017年05期

【摘要】：通過(guò)對(duì)恒星光譜進(jìn)行分析可以研究銀河系的演化與結(jié)構(gòu)等科學(xué)問(wèn)題,光譜分類是恒星光譜分析的基本任務(wù)之一。提出了一種結(jié)合非參數(shù)回歸與Adaboost對(duì)恒星光譜進(jìn)行MK分類的方法,將恒星按光譜型和光度型進(jìn)行分類,并識(shí)別其光譜型的次型。恒星光譜的光譜型及其次型代表了恒星的表面有效溫度,而光度型則代表了恒星的發(fā)光強(qiáng)度。在同一種光譜型下,光度型反映了譜線形狀細(xì)節(jié)的變化,因此光度型的分類必須在光譜型分類基礎(chǔ)上進(jìn)行。本文把光譜型的分類問(wèn)題轉(zhuǎn)化為對(duì)類別的回歸問(wèn)題,采用非參數(shù)回歸方法進(jìn)行恒星光譜型和光譜次型的分類;基于Adaboost方法組合一組K近鄰分類器進(jìn)行光度型分類,Adaboost將一組弱分類器加權(quán)組合產(chǎn)生一個(gè)強(qiáng)分類器,提升光度型的識(shí)別率。實(shí)驗(yàn)驗(yàn)證了所提出分類方法的有效性,光譜次型識(shí)別的精度達(dá)到0.22,光度型的分類正確率達(dá)到84%以上。實(shí)驗(yàn)還對(duì)比了兩種KNN方法與Adaboost方法的光度型分類,結(jié)果表明,利用KNN方法對(duì)光度型分類精度低,而基于弱分類器KNN的Adaboost方法將識(shí)別率大幅提升。
[Abstract]:The evolution and structure of the Milky way Galaxy can be studied by analyzing the spectra of stars. Spectral classification is the basic Ren Wuzhi in the spectral analysis of stars.In this paper, a method of combining non-parametric regression with Adaboost to classify the spectrum of stars is proposed. The stars are classified according to spectral type and photometric type, and the subtypes of their spectral types are identified.The spectral type and its subtype of the star spectrum represent the surface effective temperature of the star, while the luminosity type represents the luminous intensity of the star.Under the same spectral pattern, the photometric type reflects the variation of the shape details of the spectral line, so the classification of the photometric type must be based on the spectral type classification.In this paper, the classification problem of spectral type is transformed into the regression problem of category, and the classification of spectral type and spectral subtype of star is carried out by non-parametric regression method.Based on the Adaboost method, a group of K-nearest neighbor classifiers are combined for photometric classification Adaboost. A group of weak classifiers are weighted to produce a strong classifier to improve the recognition rate of photometric type.The experimental results show that the proposed classification method is effective. The accuracy of spectral subtype recognition is 0.22, and the accuracy of photometric classification is over 84%.The experiment also compares the photometric classification of two KNN and Adaboost methods. The results show that the accuracy of the photometric classification is low by using the KNN method, while the recognition rate is greatly improved by the Adaboost method based on the weak classifier KNN.
【作者單位】： 北京服裝學(xué)院基礎(chǔ)部;西安建筑科技大學(xué)理學(xué)院;中國(guó)科學(xué)院國(guó)家天文臺(tái);北京師范大學(xué)信息科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金委員會(huì)-中國(guó)科學(xué)院天文聯(lián)合基金項(xiàng)目(U1531242)資助
【分類號(hào)】：P144.1

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本文編號(hào)：1762888