ID3决策树算法实验
所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：1199KB
下载次数：12
上传日期：2018-06-19 01:09:02
上 传 者：
小强你好
说明： 决策树ID3算法实验_数据集car_databases。用python编写的决策树ID3算法，运用了Car-Evaluation的例子。BUG较少，综合了网上的优秀代码，并进一步形成自己的代码。代码基本有注释，风格良好，能够很快看懂。内含有比较规范的报告文档，包含所有流程图，说明图，以及文档风格绝对不错，无需更改，建议下载！
该算法所测试的数据集如下（已经打包在内，并已经生成xls格式，方便直接使用）：
已知：UCI标准数据集Car-Evaluation，定义了汽车性价比的4 个类别；
求：用ID3算法建立Car-Evaluation的属性描述决策树
Car-Evaluation训练数据集文件：
1. car_databases.pdf
2. car_evalution-databases.pdf
(Decision tree ID3 algorithm experiment data set car_databases. The decision tree ID3 algorithm written in Python is used in the example of Car-Evaluation. BUG is less, it combines the excellent code on the Internet, and further forms its own code. The code is basically annotated, style is good, and can read quickly. It contains a relatively standardized report document, including all flow charts, illustrations, and document styles. Absolutely, no need to change. We recommend downloading.
The data set tested by this algorithm is as follows (already packaged and has been generated in XLS format for easy use):
Known: the UCI standard dataset Car-Evaluation defines 4 categories of vehicle performance price ratio.
Use ID3 algorithm to build Car-Evaluation attribute description tree.
Car-Evaluation training dataset file:
1. car_databases.pdf
2. car_evalution-databases.pdf)
文件列表:
ID3决策树算法实验 (0, 2018-06-12)
ID3决策树算法实验\.DS_Store (6148, 2018-06-12)
__MACOSX (0, 2018-06-12)
__MACOSX\ID3决策树算法实验 (0, 2018-06-12)
__MACOSX\ID3决策树算法实验\._.DS_Store (182, 2018-06-12)
ID3决策树算法实验\car_databases.pdf (38332, 2017-10-24)
__MACOSX\ID3决策树算法实验\._car_databases.pdf (182, 2017-10-24)
ID3决策树算法实验\car_evalution-databases.csv (120807, 2017-11-02)
__MACOSX\ID3决策树算法实验\._car_evalution-databases.csv (182, 2017-11-02)
ID3决策树算法实验\car_evalution-databases.pdf (57339, 2017-10-24)
__MACOSX\ID3决策树算法实验\._car_evalution-databases.pdf (182, 2017-10-24)
ID3决策树算法实验\car_evalution-databases.xlsx (150881, 2017-11-02)
__MACOSX\ID3决策树算法实验\._car_evalution-databases.xlsx (182, 2017-11-02)
ID3决策树算法实验\ID3决策树算法实验.ppt (116224, 2017-10-25)
__MACOSX\ID3决策树算法实验\._ID3决策树算法实验.ppt (182, 2017-10-25)
.DS_Store (6148, 2017-11-02)
._.DS_Store (182, 2017-11-02)
ID3 (0, 2017-11-02)
ID3\.DS_Store (6148, 2017-11-02)
ID3 (0, 2018-06-12)
ID3\._.DS_Store (182, 2017-11-02)
ID3\code (0, 2017-11-02)
ID3\code\ID3 (0, 2017-11-03)
ID3\code\ID3\.idea (0, 2017-11-02)
ID3\code\ID3\.idea\ID3.iml (459, 2017-10-21)
ID3\code (0, 2018-06-12)
ID3\code\ID3 (0, 2018-06-12)
ID3\code\ID3\.idea (0, 2018-06-12)
ID3\code\ID3\.idea\._ID3.iml (182, 2017-10-21)
ID3\code\ID3\.idea\misc.xml (225, 2017-10-21)
ID3\code\ID3\.idea\._misc.xml (182, 2017-10-21)
ID3\code\ID3\.idea\modules.xml (258, 2017-10-21)
ID3\code\ID3\.idea\._modules.xml (182, 2017-10-21)
ID3\code\ID3\.idea\vcs.xml (189, 2017-10-21)
ID3\code\ID3\.idea\._vcs.xml (182, 2017-10-21)
ID3\code\ID3\.idea\workspace.xml (19945, 2017-10-21)
ID3\code\ID3\.idea\._workspace.xml (182, 2017-10-21)
ID3\code\ID3\._.idea (182, 2017-11-02)
... ...
Data Set Name: Anuran Calls (MFCCs)
Abstract: (less than 200 characters).
Acoustic features extracted from syllables of anuran (frogs) calls, including the family, the genus, and the species labels.
Source:
Eng. Juan Gabriel Colonna
Prof. Eduardo Freire Nakamura
Prof. Marco A. P. Cristo
Biologist and collaborator Prof. Marcelo Gordo
Universidade Federal do Amazonas, Av. General Rodrigo Octavio Jordao Ramos, 1200 - Coroado I, Manaus - AM, 69067-005, Brasil.
Number of Instances (records in your data set): 7195
Number of Attributes (fields within each record): 22
Relevant Information:
This dataset was used in several classifications tasks related to the challenge of anuran species recognition through their calls. It is a multilabel dataset with three columns of labels. This dataset was created segmenting 60 audio records belonging to 4 different families, 8 genus, and 10 species. Each audio corresponds to one specimen (an individual frog), the record ID is also included as an extra column. We used the spectral entropy and a binary cluster method to detect audio frames belonging to each syllable. The segmentation and feature extraction were carried out in Matlab. After the segmentation we got 7195 syllables, which became instances for train and test the classifier. These records were collected in situ under real noise conditions (the background sound). Some species are from the campus of Federal University of Amazonas, Manaus, others from Mata Atlantica, Brazil, and one of them from Cordoba, Argentina. The recordings were stored in wav format with 44.1kHz of sampling frequency and 32bit of resolution, which allows us to analyze signals up to 22kHz. From every extracted syllable 22 MFCCs were calculated by using 44 triangular filters. These coefficients were normalized between -1 ≤ mfcc ≤ 1. The amount of instances per class are:
Families:
Bufonidae 68
Dendrobatidae 542
Hylidae 2165
Leptodactylidae 4420
Genus:
Adenomera 4150
Ameerega 542
Dendropsophus 310
Hypsiboas 1593
Leptodactylus 270
Osteocephalus 114
Rhinella 68
Scinax 148
Species:
AdenomeraAndre 672
AdenomeraHylaedact... 3478
Ameeregatrivittata 542
HylaMinuta 310
HypsiboasCinerascens 472
HypsiboasCordobae 1121
LeptodactylusFuscus 270
OsteocephalusOopha... 114
Rhinellagranulosa 68
ScinaxRuber 148
Attribute Information:
Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an mel-frequency cepstrum (MFC). Due to each syllable has different length, every row (i) was normalized acording to MFCCs_i/(max(abs(MFCCs_i))).
Relevant Papers:
1) COLONNA, J. G.; CRISTO, M.; SALVATIERRA, M.; NAKAMURA, E. F.
An Incremental Technique for Real-Time Bioacoustic Signal Segmentation.
Expert Systems with Applications, v. 42, p. 7367-7374, 2015.
2) COLONNA, J. G.; GAMA, J.; NAKAMURA, E. F.
How to Correctly Evaluate an Automatic Bioacoustics Classification Method.
In: 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA).
Lecture Notes in Computer Science. ***6ed.: Springer International Publishing, 2016, v. , p. 37-47.
3) COLONNA, J. G.; GAMA, J.; NAKAMURA, E. F.
Recognizing Family, Genus, and Species of Anuran Using a Hierarchical Classification Approach.
Lecture Notes in Computer Science. 995ed.: Springer International Publishing, 2016, v. 9956, p. 1***-212.
4) COLONNA, J. G.; RIBAS, A. D.; SANTOS, E. M.; NAKAMURA, E. F.
Feature Subset Selection for Automatically Classifying Anuran Calls Using Sensor Networks.
In: International Joint Conference on Neural Networks, 2012, Brisbane.
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2012), 2012. p. 1-8. IEEE
5) COLONNA, J. G.; PEET, T.; FERREIRA, C. A.; JORGE, A. M.; GOMES, E. F.; GAMA, J. (2016, July).
Automatic Classification of Anuran Sounds Using Convolutional Neural Networks.
In Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering (No. C3S2E '16, pp. 73-78). ACM.
6) COLONNA, J. G.; CRISTO, M.; NAKAMURA, E. F. (2014, August).
A Distributed Approach for Classifying Anuran Species Based on Their Calls.
In Pattern Recognition (ICPR), 2014 22nd International Conference on (pp. 1242-1247). IEEE.
7) RIBAS, A. D.; COLONNA, J. G.; FIGUEIREDO, C. M. S.; NAKAMURA, E. F.
Similarity clustering for data fusion in wireless sensor networks using k-means
The 2012 International Joint Conference on Neural Networks (IJCNN 2012), p. 1-7. IEEE
8) DIAZ, J. M.; COLONNA, J. G.; SOARES, R. B.; FIGUEREIDO, C. M. S.; NAKAMURA, E. F.
Compressive sensing for efficiently collecting wildlife sounds with wireless sensor networks
21st International Conference on Computer Communications and Networks (ICCCN 2012), p. 1-7. IEEE
Citation Requests / Acknowledgements:
Please cite some of our papers.
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