WEKA 3.6-10 使用手册：命令行primer、图形用户界面和数据预处理

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WEKA Manual WEKA 是一个功能强大且广泛应用的机器学习和数据挖掘开源软件，WEKA Manual 是 WEKA 的使用文档，提供了非常详细的使用说明，涵盖了 WEKA 的所有方面，包括命令行界面、图形用户界面、数据预处理、分类器、过滤器等。 WEKAManual 由 Remco R. Bouckaert、Eibe Frank、Mark Hall、Richard Kirkby、Peter Reutemann、Alex Seewald 和 David Scuse 共同编写，于 2013 年 7 月 31 日发布，当前版本为 3.6-10。在 WEKA Manual 中，作者详细介绍了 WEKA 的基本概念、数据集、分类器、过滤器等，提供了丰富的示例和实践经验，帮助用户快速掌握 WEKA 的使用。 WEKA Manual 分为两大部分，第一部分是命令行界面，提供了 WEKA 命令行界面的基本概念、使用方法和示例；第二部分是图形用户界面，介绍了 WEKA 的图形用户界面、数据预处理、分类器、过滤器等。在命令行界面部分，作者详细介绍了 WEKA 命令行界面的使用方法，包括基本概念、数据集、分类器、过滤器等，提供了丰富的示例和实践经验。在图形用户界面部分，作者详细介绍了 WEKA 的图形用户界面，包括用户界面、状态框、日志按钮、WEKA 状态图标、图形输出等，提供了丰富的示例和实践经验。 WEKA Manual 是 WEKA 的官方使用文档，提供了非常详细的使用说明，涵盖了 WEKA 的所有方面，是 WEKA 用户的必备参考资料。知识点： 1. WEKA 是一个功能强大且广泛应用的机器学习和数据挖掘开源软件。 2. WEKA Manual 是 WEKA 的使用文档，提供了非常详细的使用说明。 3. WEKA Manual 由 Remco R. Bouckaert、Eibe Frank、Mark Hall、Richard Kirkby、Peter Reutemann、Alex Seewald 和 David Scuse 共同编写。 4. WEKA Manual 分为两大部分，第一部分是命令行界面，第二部分是图形用户界面。 5. 命令行界面部分包括基本概念、数据集、分类器、过滤器等。 6. 图形用户界面部分包括用户界面、状态框、日志按钮、WEKA 状态图标、图形输出等。 7. WEKA Manual 提供了丰富的示例和实践经验，帮助用户快速掌握 WEKA 的使用。关键词：WEKA、Machine Learning、Data Mining、命令行界面、图形用户界面、数据预处理、分类器、过滤器。

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.Classifier

class. Surprisingly little is needed for a basic classiﬁer: a routine which gen-

erates a classiﬁer model from a training dataset (= buildClassifier) and

another routine which evaluates the generated model on an unseen test da taset

(= classifyInstance), or generates a probability distribution for all classes

(= distributionForInstance).

A classiﬁer model is an ar bitrary 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 trivia l classiﬁer, but it gives a

lower bound on the per fo rmance of a given dataset which sho uld be signiﬁcantly

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

well the class can b e 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 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 proportio n 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 o f equal

size. In each iter ation, 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 be non-stratiﬁed,

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 c oncerned 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 da taset. 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 c lass 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 sca ling.

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 experiments, 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 class 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 cla ss 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 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 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 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 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 deta iled 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 s e lected classiﬁers in WEKA. Other classiﬁers below

weka.classiﬁers may also be used. This is more easy to see in the Explor e r 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 Logistic Regression.

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WEKA 3.6-10 使用手册：命令行primer、图形用户界面和数据预处理

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