Weka 3.7.11 用户手册：命令行与GUI指南

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"WekaManual3.7.11是Weka数据挖掘工具的用户手册，由Remco Bouckaert、Eibe Frank、Mark Hall等人编写。Weka是一款开源的数据挖掘软件，主要用于机器学习和数据挖掘任务。该手册涵盖了命令行界面的使用以及图形用户界面的操作，提供对数据集、分类器、过滤器等核心概念的详细解释，并指导用户如何安装和管理额外的算法包。" 在Weka 3.7.11的手册中，主要分为两个部分：命令行界面和图形用户界面。一、命令行界面 1. 命令行primer - 引入了如何在命令行下操作Weka的基本概念，这对于自动化处理大数据集或集成到其他系统中非常有用。 - 基本概念包括数据集、分类器、过滤器和Weka特定的类库（如weka.filtres和weka.classifiers）。 2. 数据集 - 数据集是Weka的核心，包含了用于学习和预测的实例。手册详细介绍了数据集的结构和格式。 3. 分类器 - 分类器是Weka中的关键组件，它们用于从训练数据中学习模型，然后用于预测新数据的类别。 4. 过滤器 - 过滤器用于预处理数据，例如特征选择、归一化或转换，以改善模型性能。 5. 示例和额外包管理 - 提供了使用示例来说明如何执行常见的数据挖掘任务。 - 描述了如何通过包管理器添加和管理额外的机器学习算法包。二、图形用户界面 1. 启动Weka - 详述了如何启动Weka的图形界面，这对于初学者和非程序员来说更直观易用。 2. 包管理器 - 主窗口介绍，展示了用户如何与Weka的包管理系统进行交互。 - 安装和移除包的步骤，让用户能够扩展Weka的功能，获取最新的算法和工具。 - 非官方包的安装说明，允许用户探索社区提供的附加组件。 - 使用HTTP代理和替代中央包元数据仓库的设置，适应不同的网络环境和需求。此手册还涵盖了版权信息，指出Weka是受到GNU General Public License version 3的保护，这意味着用户可以自由地使用、修改和分发这个软件。通过这些内容，用户不仅可以了解如何有效地使用Weka进行数据挖掘和机器学习任务，还可以掌握如何扩展Weka以适应更广泛的项目需求。无论是对命令行还是图形界面的熟悉，都能帮助用户充分利用这款强大的工具。

16 CHAPTER 1. A COMMAND-LINE PRIMER

1.2.2 Classiﬁer

Any lear ning algorithm in WE KA is derived from the abstract weka.classifiers.AbstractClassifier

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

little is needed for a basic c lassiﬁer: a routine which generates a c lassiﬁer 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 c reation of this map-

ping, or model, diﬀers from classiﬁer to classiﬁer. For example, 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 can 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 Ins tances 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 clas siﬁers. The

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

rectly pre dicted 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 estimates, hold-out estimates may be computed 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-validation signiﬁes that n is equal

to the number of examples. Out of necessity, loo cv has to b e non-stratiﬁed,

i.e. the class distributions in the test s e t 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 co nce rned with classes that transform datasets –

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

and so on. This package oﬀers useful suppor t 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 datas e t. If any of these parameters is not given, s tandard

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 cla ss 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 clas s informa tio n, via Fayyad & Ira ni’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 transfor m 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 da tasets, 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 Res ample. 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 pre sence or word count (=

-C) within the string. -W can be used to set an approximate limit o n the numbe r

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

This ﬁlter is useful for text mining.

Obfuscate renames the datase t name, all attribute names and nominal attribute

values. This is intended for exchanging s ensitive 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 dataset:

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 given dataset. The class 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 ca n

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 compatible 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 detailed performance description via precision, recall,

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 Na iveBayes,

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 b elow

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

• trees.J48 A c lone of the C4.5 decision tree learner

• bayes.NaiveBayes A Naive B ayesian 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 Log istic Regression.

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