Weka3.7数据挖掘工具手册

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"Weka3.6帮助文档是关于开源数据挖掘工具Weka的详细指南，由Remco Bouckaert、Eibe Frank、Mark Hall等人编写。这个版本是3.7.2，适用于2010年7月。该文档遵循GNU General Public License v2发布。" Weka是一个强大的数据挖掘软件，主要由Java编程语言开发，它提供了丰富的机器学习算法和数据预处理工具。Weka被广泛应用于数据分析和预测模型构建，尤其在教育和研究领域非常流行。以下是文档的主要内容： I. The Command-line 部分： 1.1 Introduction：这部分介绍了如何通过命令行界面使用Weka，这对于自动化任务和脚本编写特别有用。 1.2 Basic concepts： - Dataset：Weka中的数据集包含了用于分析的实例，每个实例都有若干属性和一个类别标签。 - Classiﬁer：分类器是Weka的核心部分，它使用训练数据学习模式，然后可以对新的未标记数据进行预测。 - weka.filters：过滤器模块用于预处理数据，如转换、选择或清洗属性，以提高模型性能。 - weka.classifiers：包含各种分类算法，如决策树、贝叶斯网络、支持向量机等。 1.3 Examples：提供了使用不同算法和过滤器的示例，帮助用户理解Weka的基本用法。 1.4 Additional packages and the package manager：介绍了如何安装和管理额外的Weka扩展包，以获取更多的功能。 II. The Graphical User Interface (GUI) 部分： 2. Launching WEKA：解释了如何启动Weka的图形用户界面。 3. Package Manager： - Main window：显示了包管理器的主界面，用户在此界面中可以看到可用的包和已安装的包。 - Installing and removing packages：详细说明了如何添加新包以及移除不再需要的包。 - Using a http proxy：如果用户在受限制的网络环境中，这部分指导如何配置代理以访问包库。 - Using an alternative central package metadata repository：用户可以自定义包元数据仓库，以获取非官方的或特定组织的包。 4. Simple CLI：这部分介绍了Weka的简单命令行界面（SimpleCLI），提供了一种简化的方法来执行常见的数据挖掘任务。通过Weka3.6的帮助文档，用户不仅可以学习到如何使用各种数据挖掘算法，还可以了解如何通过命令行和图形界面工具有效地操作和管理数据，以及如何扩展Weka的功能。这使得无论是初学者还是经验丰富的数据科学家都能从中受益。

16 CHAPTER 1. A COMMAND-LINE PRIMER

1.2.2 Classiﬁer

Any learning algorithm in WEKA is derived from the abstract weka.classifiers.AbstractClassifier

class. This, in turn, implements weka.classifiers.Classifier. Surprisingly

little is needed for a basic classiﬁer: a ro utine which generates a classiﬁer model

from a training dataset (= buildClassifier) and another routine which eval-

uates the generated model on an unseen test dataset (= classifyInstance), or

generates a probability distribution for all classes (= distributionForInstance).

A classiﬁer model is an arbitrary complex mapping from all-but-one dataset

attributes to the class attribute. The speciﬁc form and creation of this map-

ping, or model, diﬀers from classiﬁer to classiﬁer. For e xample, ZeroR’s (=

weka.classifiers.rules.ZeroR) model just consists of a single value: the

most common class, or the median of all numeric values in case of predicting a

numer ic value (= regression learning). ZeroR is a trivial classiﬁer, but it gives a

lower bound on the performance of a given dataset which should be signiﬁcantly

improved by more complex classiﬁers. As such it is a reasonable test on how

well the class ca n be predicted without considering the other attributes.

Later, we will explain how to interpret the output from classiﬁers in detail –

for now just focus on the Correctly Classiﬁed Instances in the section Stratiﬁed

cross-validation and notice how it improves from ZeroR to J48:

java weka.classifiers.rules.ZeroR -t weather.arff

java weka.classifiers.trees.J48 -t weather.arff

There are various approaches to determine the performance of classiﬁers. The

performance can most simply be measured by counting the proportion of cor-

rectly predicted examples in an unseen test dataset. This value is the accuracy,

which is also 1-ErrorRate. Both terms are used in literature.

The simplest case is using a training set and a test set which are mutually

independent. This is referred to as hold-out estimate. To estimate variance in

these performance e stimates, hold-o ut estimates may be co m puted by repeatedly

resampling the same dataset – i.e. randomly reordering it and then splitting it

into training and test sets with a speciﬁc proportion of the examples, collecting

all estimates on test data and computing average and standard deviation of

accuracy.

A more elaborate method is cross-validation. Here, a number of folds n is

sp e c iﬁed. The dataset is randomly reordered and then split into n folds of equal

size. In each iteration, one fold is used for testing and the other n-1 folds are

used for training the classiﬁer. The test results are collected a nd average d over

all folds. This gives the cross-validation estimate of the accuracy. The folds can

be purely random o r slig htly modiﬁed to create the same class distributions in

each fold as in the complete data set. In the latter case the cross-validation is

called stratiﬁed. Leave-one-out (= loo) cross-validatio n signiﬁes that n is equal

to the number of examples. Out of necessity, loo cv has to be non-s tratiﬁed,

i.e. the class distributions in the test set are not related to those in the training

data. Therefore loo cv tends to give less reliable results. However it is still

quite useful in dealing with small datas e ts since it utilizes the greatest amount

of training data from the dataset.

1.2. BASIC CONCEPTS 17

1.2.3 weka.ﬁlters

The weka.filters package is concerned with classes that transform datasets –

by removing or adding attributes, resampling the dataset, removing examples

and so on. T his package oﬀers useful support for data preprocessing, which is

an important step in machine learning.

All ﬁlters oﬀer the options -i for specifying the input dataset, and -o for

sp e c ifying the output data set. If any of these parameters is not given, standard

input and/or standard output will be read from/written to. Other parameters

are speciﬁc to each ﬁlter and can be found out via -h, as with any other class.

The weka.filters packa ge is organized into supervised and unsupervised

ﬁltering, b oth of which are again subdivided into instance and attribute ﬁltering.

We will discuss each of the four subsections separately.

weka.ﬁlters.supervised

Classes below weka.filters.supervised in the class hierarchy are for super-

vised ﬁltering, i.e., taking advantage of the class information. A class must be

assigned via -c, for WEKA default behaviour use -c last.

weka.ﬁlters.supervised.attribute

Discretize is used to discretize numeric attributes into nominal ones, based

on the class information, via Fayyad & Irani’s MDL method, or optionally

with Kononeko’s MDL method. At least some learning schemes or classiﬁers

can only process nominal data, e.g. weka.classifiers.rules.Prism; in some

cases discretization may also reduce learning time.

java weka.filters.supervised.attribute.Discretize -i data/iris.arff \

-o iris-nom.arff -c last

java weka.filters.supervised.attribute.Discretize -i data/cpu.arff \

-o cpu-classvendor-nom.arff -c first

NominalToBinary encodes all nominal attributes into binary (two- valued) at-

tributes, which can be used to transform the dataset into a purely numeric

representation, e.g. for visualization via multi-dimensional scaling.

java weka.filters.supervised.attribute.NominalToBinary \

-i data/contact-lenses.arff -o contact-lenses-bin.arff -c last

Keep in mind that most classiﬁers in WEKA utilize transformation ﬁlters in-

ternally, e.g. Logistic and SMO, so you will usually not have to use these ﬁlters

explicity. However, if you plan to run a lot of exp e riments, pre-applying the

ﬁlters yourself may improve runtime performance.

weka.ﬁlters.supervised.instance

Resample creates a stratiﬁed subsample o f the given dataset. This means that

overall class distributions are approximately retained within the sample. A bias

towards uniform class distribution can be speciﬁed via -B.

java weka.filters.supervised.instance.Resample -i data/soybean.arff \

-o soybean-5%.arff -c last -Z 5

java weka.filters.supervised.instance.Resample -i data/soybean.arff \

-o soybean-uniform-5%.arff -c last -Z 5 -B 1

18 CHAPTER 1. A COMMAND-LINE PRIMER

StratifiedRemoveFolds creates stratiﬁed cross- validation folds of the given

dataset. This means that by default the cla ss distributions are approximately

retained within each fold. The following example splits soybean.a rﬀ into strat-

iﬁed training and test datasets, the latter consisting of 25% (= 1/4) of the

data.

java weka.filters.supervised.instance.StratifiedRemoveFolds \

-i data/soybean.arff -o soybean-train.arff \

-c last -N 4 -F 1 -V

java weka.filters.supervised.instance.StratifiedRemoveFolds \

-i data/soybean.arff -o soybean-test.arff \

-c last -N 4 -F 1

weka.ﬁlters.unsupervis ed

Classes below weka.filters.unsupervised in the class hierarchy are for unsu-

pervised ﬁltering, e.g. the non-stratiﬁed version of Resample. A class attribute

should not be assigned here.

weka.ﬁlters.unsupervis ed.attribute

StringToWordVector transfor ms string attributes into word vectors, i.e. creat-

ing one attribute for each word which either encodes presence or word count (=

-C) within the string. -W can be used to set an approximate limit on the number

of words. When a class is assigned, the limit applies to each class separately.

This ﬁlter is useful for text mining.

Obfuscate renames the dataset name, all attribute na mes and nominal attribute

values. This is intended for exchanging sensitive datasets without giving away

restricted information.

Remove is intended for explicit deletion of attributes from a dataset, e.g. for

removing attributes of the iris datase t:

java weka.filters.unsupervised.attribute.Remove -R 1-2 \

-i data/iris.arff -o iris-simplified.arff

java weka.filters.unsupervised.attribute.Remove -V -R 3-last \

-i data/iris.arff -o iris-simplified.arff

weka.ﬁlters.unsupervis ed.instance

Resample creates a non-stratiﬁed subsample of the given dataset, i.e. random

sampling without regard to the class information. Otherwise it is equivalent to

its supervised variant.

java weka.filters.unsupervised.instance.Resample -i data/soybean.arff \

-o soybean-5%.arff -Z 5

RemoveFoldscreates cross-validation folds of the g iven dataset. The clas s distri-

butions are not retained. The following example splits soybean.arﬀ into training

and test datasets, the latter consisting of 25% (= 1/4) of the data.

java weka.filters.unsupervised.instance.RemoveFolds -i data/soybean.arff \

-o soybean-train.arff -c last -N 4 -F 1 -V

java weka.filters.unsupervised.instance.RemoveFolds -i data/soybean.arff \

-o soybean-test.arff -c last -N 4 -F 1

RemoveWithValues ﬁlters instances according to the value of an attribute.

java weka.filters.unsupervised.instance.RemoveWithValues -i data/soybean.arff \

-o soybean-without_herbicide_injury.arff -V -C last -L 19

1.2. BASIC CONCEPTS 19

1.2.4 weka.classiﬁers

Classiﬁers are at the core of WEKA. There are a lot of common options for

classiﬁers, most of which are related to evaluation purposes. We will focus on

the most important ones. All others including classiﬁer-speciﬁc parameters can

be found via -h, as usual.

-t sp e c iﬁes the training ﬁle (ARFF format)

-T

sp e c iﬁes the test ﬁle in (ARFF format). If this parameter is miss-

ing, a cr ossvalidation will be performed (default: ten-fold cv)

-x

This parameter determines the number of folds for the cross-

validation. A cv will only be performed if -T is missing.

-c

As we already know from the weka.ﬁlters section, this parameter

sets the class variable with a one-based index.

-d

The model after training can be saved via this parameter. Each

classiﬁer has a diﬀerent binary format for the model, so it can

only be read back by the exact sa me classiﬁer on a compatible

dataset. Only the model on the training set is saved, not the

multiple models generated via cross-validation.

-l

Loads a pr e viously saved model, usually for testing on new, pre-

viously unseen data. In that case, a co mpatible test ﬁle should be

sp e c iﬁed, i.e. the same attributes in the same order.

-p #

If a test ﬁle is speciﬁed, this parameter shows you the predictions

and one attribute (0 for none) for all test instances.

-i

A more deta iled performance description via precision, reca ll,

true- and false positive rate is additionally o utput with this pa-

rameter. All these values can also be computed from the confusion

matrix.

-o

This parameter switches the human-readable output of the model

description oﬀ. In case of support vector machines or NaiveBayes,

this makes some se nse unless you want to parse and visualize a

lot of information.

We now give a short list of selected classiﬁers in WEKA. Other classiﬁers below

weka.classiﬁers may also be used. This is more easy to see in the Explorer GUI.

• trees.J48 A clone of the C4.5 decision tr e e learner

• bayes.NaiveBayes A Naive Bayesian learner. -K switches on kernel den-

sity estimation for numerical attributes which often improves performance.

• meta.ClassificationViaRegression -W functions.LinearRegression

Multi-response linear regression.

• functions.Logistic Logistic Regression.

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