数据挖掘技术在文本分类和生物信息学中的应用
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摘要
数据挖掘就是从大量的、不完全的、有噪声的、模糊的、随机的数据库中提取隐含在其中的、人们事先不知道的、但又是潜在有用的信息和知识的过程。它是一个涉及面很广的交叉学科,包括机器学习、数理统计、人工智能、神经网络、数据库、模式识别、粗糙集和模糊数学等相关技术。本文基于数据挖掘的一些相关技术,做了如下几个方面的工作:(1)针对标准互信息和tf.idf特征权重公式的缺点提出了改进方法,仿真实验表明,改进的方法明显提高了宏观准确率、宏观召回率和宏观F1值;(2)针对标准tf.idf方法估算特征权重的盲目性,提出了基于实数域粗糙集理论的特征频率重要度加权方法,仿真实验表明,这种加权方法改善了样本空间的分布状态,使同类的样本更加紧凑,不同类样本更加松散,仿真实验表明,明显提高了文本分类的效果;(3)针对文本分类存在的高维特征空间和高度特征冗余,提出了一种基于互信息和信息熵对的特征选择方法,仿真实验表明,基于该方法的文本分类效果比MI方法和CHI方法都更有效,利用该方法进行特征选择的分类效果接近代表分类水平的支持向量机;(4)针对使用计算机为新测序的生物序列进行功能注释的效果较差的实际,基于GO数据库和BLAST程序,提出了一种基于可变精度粗糙集理论为新的生物序列进行功能注释的方法,仿真实验表明,提出的方法具有较高的准确率、召回率和调和均值;(5)针对目前人类种群进化研究方法的局限性,提出了基于Y染色体SNP基因型频率数据建立人类种群进化关系的新方法,仿真实验表明,本文方法支持“走出非洲”假说,为人类种群进化研究提供了一个新思路。
Data Mining is the process to abstract hidden, potentially useful information and knowledge from massive, incomplete, noisy, fuzzy and random data base. It is inter disciplinary subject including: machine learning, statistics, AI, ANN, data base, pattern reorganization, rough set, fuzzy math, and so on. In this paper, some applications of the techniques of data mining in text classification and bioinformatics are studied. For text classification, there are three mainly contributed works in the paper: developed an integration method of feature selection and weight evaluation; proposed a feature selection method considered redundancy features; developed a feature frequency weighting method based on Variable Precision Rough Set. For bioinformatics, there are 2 mainly contributed works as well: proposed a gene annotation method based on Variable Precision Rough Set; developed a method to construct the evolution tree of human populations according to the SNP frequency data set of Y chromosome of humans. The details are as follows:
(1) Considered the fact that most of low requency words are noise data, a filtering low frequency words method is proposed. The experiment results show that this method could improve the effectiveness of text classification. Focused on the Mutaul Information based feature selection method and tf.idf feature weight evaluation method, two improved methods are developed, respectively. By using Rocchio,kNN and SVM classifiers, the improved methods are applied to the banchmark text set Reuters-21578 Top10. Numerical results show that the combination of the two improved methods are effective, the macro accuracy, macro recall rate and the macro F1 value are all superior to those of other methods.
(2) Define an important concept, namely that the importance degree of feature frequency based on the real rough set theory. Based on this concept, a novel weighting method for feature frequency is proposed, which considers the decisive information when we evaluate the contribution of feature frequency, and therefore it could obtain more objective evaluation results. Experimental results show that the proposed method could improve the distribution the samples’space and make the samples of the same kind more compact, and those ones of different kinds more loose; and the values of macro accuracy, macro recall rate and the macro F1 are all significantly improved.
(3) Focused on the high dimensions of the feature space and the high feature redundancy of text classification problems, a Mutual Information and Information Entropy Pair Based Feature Selection Method is developed. Using developed relationship between information construction feature and the classes, the redundant features could be reduced greatly according to the mutual entropy of feature pairs. Two different machine learning methods, namely native Bayes Networks and kNN methods, are applied to the banchmark data sets of Reuters-21578 Top10 and WebKB. Experimental results show that the proposed method is more efficient than MI and CHI.
(4) Using experimental methods to determine the sequence funcitons is too much expensive, and couldn’t be used for the large scale annotation. TOP BLAST method is a simple and commanly used computational method. Compared with other compational methods, the precision, recall rate and harmonic mean are all higher, but the absolute values are still low. In this paper, a sequence function annotation method using the variable precision rough set theory based on the GO data base and BLAST software is proposed. The numerical results show that the proposed method could obtain higher macro accuracy than TOP BLAST, and similar macro recall rate and the macro F1 value with TOP BLAST.
(5) The different order of genome nucleotides reflects the distance between the different population’s evolution relationships. To construct the phylogenetic tree according to the level of differences between DNA molecules, it can approve the evolution relationships between different populations set by the traditional taxonomy. Since single nucleotide polymorphism data conserved most of the DNA molecule information, and most of the chromosome Y is none-recombination area, low mutation rate, it is able to record the evolution incident dutifully. Therefore a new method to construct the evolution tree of human populations according to the SNP frequency data set of Y chromosome of humans is developed in the paper. The numerical results show that the proposed method is supportive to the theory of“walking out of Africa”. The method offers a new idea for the research of human evolution.
To sum up, this paper develops an integration method of improved MI and improved feature weighting methods, a feature selection method for small redundancy features and a novel weighting method for feature frequency based on the real rough set theory, respectively. The work enriches the methods of feature selection and feature weight evaluation, also brings some new ideas to the text classification key techniques. This paper also proposes a gene annotation method based on the variable precision rough set, which has better performance for noisy data, and promote the realization of automatic annotation method. At last, a new method to construct the evolution tree of human populations according to the SNP frequency data set of Y chromosome of humans is developed, which supports the well known theory of“walking out of Africa”, and offers a novel idea for the research of human evolution.
Data Mining is the process to abstract hidden, potentially useful information and knowledge from massive, incomplete, noisy, fuzzy and random data base. It is inter disciplinary subject including: machine learning, statistics, AI, ANN, data base, pattern reorganization, rough set, fuzzy math, and so on. In this paper, some applications of the techniques of data mining in text classification and bioinformatics are studied. For text classification, there are three mainly contributed works in the paper: developed an integration method of feature selection and weight evaluation; proposed a feature selection method considered redundancy features; developed a feature frequency weighting method based on Variable Precision Rough Set. For bioinformatics, there are 2 mainly contributed works as well: proposed a gene annotation method based on Variable Precision Rough Set; developed a method to construct the evolution tree of human populations according to the SNP frequency data set of Y chromosome of humans. The details are as follows:
(1) Considered the fact that most of low requency words are noise data, a filtering low frequency words method is proposed. The experiment results show that this method could improve the effectiveness of text classification. Focused on the Mutaul Information based feature selection method and tf.idf feature weight evaluation method, two improved methods are developed, respectively. By using Rocchio,kNN and SVM classifiers, the improved methods are applied to the banchmark text set Reuters-21578 Top10. Numerical results show that the combination of the two improved methods are effective, the macro accuracy, macro recall rate and the macro F1 value are all superior to those of other methods.
(2) Define an important concept, namely that the importance degree of feature frequency based on the real rough set theory. Based on this concept, a novel weighting method for feature frequency is proposed, which considers the decisive information when we evaluate the contribution of feature frequency, and therefore it could obtain more objective evaluation results. Experimental results show that the proposed method could improve the distribution the samples’space and make the samples of the same kind more compact, and those ones of different kinds more loose; and the values of macro accuracy, macro recall rate and the macro F1 are all significantly improved.
(3) Focused on the high dimensions of the feature space and the high feature redundancy of text classification problems, a Mutual Information and Information Entropy Pair Based Feature Selection Method is developed. Using developed relationship between information construction feature and the classes, the redundant features could be reduced greatly according to the mutual entropy of feature pairs. Two different machine learning methods, namely native Bayes Networks and kNN methods, are applied to the banchmark data sets of Reuters-21578 Top10 and WebKB. Experimental results show that the proposed method is more efficient than MI and CHI.
(4) Using experimental methods to determine the sequence funcitons is too much expensive, and couldn’t be used for the large scale annotation. TOP BLAST method is a simple and commanly used computational method. Compared with other compational methods, the precision, recall rate and harmonic mean are all higher, but the absolute values are still low. In this paper, a sequence function annotation method using the variable precision rough set theory based on the GO data base and BLAST software is proposed. The numerical results show that the proposed method could obtain higher macro accuracy than TOP BLAST, and similar macro recall rate and the macro F1 value with TOP BLAST.
(5) The different order of genome nucleotides reflects the distance between the different population’s evolution relationships. To construct the phylogenetic tree according to the level of differences between DNA molecules, it can approve the evolution relationships between different populations set by the traditional taxonomy. Since single nucleotide polymorphism data conserved most of the DNA molecule information, and most of the chromosome Y is none-recombination area, low mutation rate, it is able to record the evolution incident dutifully. Therefore a new method to construct the evolution tree of human populations according to the SNP frequency data set of Y chromosome of humans is developed in the paper. The numerical results show that the proposed method is supportive to the theory of“walking out of Africa”. The method offers a new idea for the research of human evolution.
To sum up, this paper develops an integration method of improved MI and improved feature weighting methods, a feature selection method for small redundancy features and a novel weighting method for feature frequency based on the real rough set theory, respectively. The work enriches the methods of feature selection and feature weight evaluation, also brings some new ideas to the text classification key techniques. This paper also proposes a gene annotation method based on the variable precision rough set, which has better performance for noisy data, and promote the realization of automatic annotation method. At last, a new method to construct the evolution tree of human populations according to the SNP frequency data set of Y chromosome of humans is developed, which supports the well known theory of“walking out of Africa”, and offers a novel idea for the research of human evolution.
引文
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