文本分类算法综述：特征提取、方法与评估

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文本分类算法综述随着信息时代的快速发展，大量复杂文档和文本数据的处理需求日益增长，对于准确分类这些文本内容，机器学习技术的应用显得尤为重要。本文档《Text Classification Algorithms _ A Survey》提供了对文本分类算法的深入探讨，涵盖了以下几个关键方面： 1. **文本特征提取**：文本数据的特征是算法的基础，包括词袋模型（Bag of Words）、TF-IDF（Term Frequency-Inverse Document Frequency）、n-gram、词嵌入（Word Embeddings）等方法，这些都用于捕捉文本中的语义和结构信息。 2. **维度ality reduction**：为了处理高维稀疏数据，文章讨论了诸如词向量降维（如PCA、LSA或LDA）、t-SNE等技术，它们能减少特征空间的维度，提高算法效率并防止过拟合。 3. **现有算法与技术**：文本分类算法涵盖了一系列机器学习和深度学习方法，如朴素贝叶斯（Naive Bayes）、支持向量机（SVM）、决策树（Decision Trees）、随机森林（Random Forest）、神经网络（如RNN、LSTM、BERT等）以及卷积神经网络（CNN）。每种方法都有其优势和适用场景。 4. **深度学习的兴起**：近年来，深度学习在文本分类领域的表现尤为突出，特别是通过预训练模型如BERT、ELMo和GPT等，能够捕捉上下文信息和复杂的语言模式，从而提升分类性能。 5. **评估方法**：文本分类的性能通常通过精确率（Precision）、召回率（Recall）、F1分数、ROC曲线和AUC值等指标来衡量。此外，交叉验证、网格搜索和超参数调优也是优化算法性能的关键步骤。 6. **成功案例与挑战**：尽管这些算法在自然语言处理任务中取得了显著成果，但仍面临挑战，如噪声数据的影响、多义词处理、文本长度不一等问题，以及如何在大规模数据和实时性需求之间找到平衡。《Text Classification Algorithms _ A Survey》是一篇详尽的研究论文，它为理解文本分类领域的最新进展和技术提供了宝贵的参考，对于那些希望在信息检索、情感分析、新闻分类等应用场景中应用机器学习的人来说，具有很高的实用价值。通过阅读这篇综述，读者可以掌握从数据预处理到模型选择和优化的整个流程，并了解如何根据实际需求选择合适的算法。

Information 2019, 10, 150 12 of 68

PCA can be used as a pre-processing tool to reduce the dimension of a data set before running a

supervised learning algorithm on it (

(i)

as inputs). PCA is also a valuable tool as a noise reduction

algorithm and can be helpful in avoiding the over-ﬁtting problem [

]. kernel principal component

analysis (KPCA) is another dimensionality reduction method that generalizes linear PCA into the

nonlinear case by using the kernel method [71].

3.1.2. Independent Component Analysis (ICA)

Independent component analysis (ICA) was introduced by H. Jeanny [

]. This technique was

then further developed by C. Jutten and J. Herault [

]. ICA is a statistical modeling method

where the observed data are expressed as a linear transformation [

]. Assume that 4

linear

mixtures (x

, x

, . . . , x

) are observed where independent components:

= a

+ a

+ . . . + a

∀j (13)

The vector-matrix notation is written as:

X = As (14)

Denoting them by a

, the model can also be written [75] as follows:

X =

∑

i=1

(15)

3.2. Linear Discriminant Analysis (LDA)

LDA is a commonly used technique for data classiﬁcation and dimensionality reduction [

]. LDA

is particularly helpful where the within-class frequencies are unequal and their performances have been

evaluated on randomly generated test data. Class-dependent and class-independent transformation

are two approaches to LDA in which the ratio of between class variance to within class variance and

the ratio of the overall variance to within class variance are used respectively [77].

Let

∈ R

which be

-dimensional samples and

∈ {

1, 2, ...,

be associated target or

output [

], where

is the number of documents and

is the number of categories. The number of

samples in each class is calculated as follows:

∑

l=1

(16)

where

∑

x∈w

(x −µ

)(x −µ

)

, µ

∑

x∈w

x (17)

The generalization between the class scatter matrix is deﬁned as follows:

∑

i=1

(µ

−µ)(µ

−µ)

(18)

where

µ =

∑

∀x

x (19)

Respect to c −1 projection vector of w

that can be projected into W matrix:

W =

[

|. . . |w

c−1

]

(20)

= w

x (21)

Information 2019, 10, 150 14 of 68

The objective function, given by the Kullback–Leibler [81,82] divergence, is deﬁned as follows:

= H

∑

(WH)

(32)

= W

∑

(WH)

(33)

∑

(34)

This NMF-based dimensionality reduction contains the following 5 steps [

] (step VI is optional

but commonly used in information retrieval:

(I)

Extract index term after pre-processing stem like feature extraction and text cleaning as

discussed in Section 2. Then we have n documents with m features;

(II)

Create

documents (

d ∈ {d

. . .

}

), where vector

= L

× G

where

refers to local

weights of i

−th

term in document j, and G

is global weights for document i;

(III) Apply NMF to all terms in all documents one by one;

(IV) Project the trained document vector into r-dimensional space;

(V) Using the same transformation, map the test set into the r-dimensional space;

(VI) Calculate the similarity between the transformed document vectors and a query vector.

3.4. Random Projection

Random projection is a novel technique for dimensionality reduction which is mostly used for

high volume data set or high dimension feature spaces. Texts and documents, especially with weighted

feature extraction, generate a huge number of features. Many researchers have applied random

projection to text data [

] for text mining, text classiﬁcation, and dimensionality reduction. In this

section, we review some basic random projection techniques. As shown in Figure 5, the overview of

random projection is shown.

3.4.1. Random Kitchen Sinks

The key idea of random kitchen sinks [

] is sampling via monte carlo integration [

]

to approximate the kernel as part of dimensionality reduction. This technique works only for

shift-invariant kernel:

K(x, x

) =< φ(x), φ(x

) >≈ K(x − x

) (35)

where shift-invariant kernel, which is an approximation kernel of:

K(x − x

) = z(x)z(x

) (36)

K(x, x

) =

P(w)e

(x−x

)

(37)

where

is the target number of samples,

P(w)

is a probability distribution,

stands for random

direction, and w ∈ R

F×D

where F is the number of features and D is the target.

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