語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Learning from imbalanced data sets
~
Fernandez, Alberto.
Learning from imbalanced data sets
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Learning from imbalanced data sets/ by Alberto Fernandez ... [et al.].
其他作者:
Fernandez, Alberto.
出版者:
Cham :Springer International Publishing : : 2018.,
面頁冊數:
xviii, 377 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
標題:
Machine learning. -
電子資源:
https://doi.org/10.1007/978-3-319-98074-4
ISBN:
9783319980744
Learning from imbalanced data sets
Learning from imbalanced data sets
[electronic resource] /by Alberto Fernandez ... [et al.]. - Cham :Springer International Publishing :2018. - xviii, 377 p. :ill., digital ;24 cm.
1 Introduction to KDD and Data Science -- 2 Foundations on Imbalanced Classification -- 3 Performance measures -- 4 Cost-sensitive Learning -- 5 Data Level Preprocessing Methods -- 6 Algorithm-level Approaches -- 7 Ensemble Learning -- 8 Imbalanced Classification with Multiple Classes -- 9 Dimensionality Reduction for Imbalanced Learning -- 10 Data Intrinsic Characteristics -- 11 Learning from Imbalanced Data Streams -- 12 Non-Classical Imbalanced Classification Problems -- 13 Imbalanced Classification for Big Data -- 14 Software and Libraries for Imbalanced Classification.
This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.
ISBN: 9783319980744
Standard No.: 10.1007/978-3-319-98074-4doiSubjects--Topical Terms:
561253
Machine learning.
LC Class. No.: Q325.5
Dewey Class. No.: 006.31
Learning from imbalanced data sets
LDR
:03589nam a2200337 a 4500
001
929568
003
DE-He213
005
20181024021624.0
006
m d
007
cr nn 008maaau
008
190626s2018 gw s 0 eng d
020
$a
9783319980744
$q
(electronic bk.)
020
$a
9783319980737
$q
(paper)
024
7
$a
10.1007/978-3-319-98074-4
$2
doi
035
$a
978-3-319-98074-4
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
072
7
$a
TJFM1
$2
thema
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.L438 2018
245
0 0
$a
Learning from imbalanced data sets
$h
[electronic resource] /
$c
by Alberto Fernandez ... [et al.].
260
$a
Cham :
$c
2018.
$b
Springer International Publishing :
$b
Imprint: Springer,
300
$a
xviii, 377 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
1 Introduction to KDD and Data Science -- 2 Foundations on Imbalanced Classification -- 3 Performance measures -- 4 Cost-sensitive Learning -- 5 Data Level Preprocessing Methods -- 6 Algorithm-level Approaches -- 7 Ensemble Learning -- 8 Imbalanced Classification with Multiple Classes -- 9 Dimensionality Reduction for Imbalanced Learning -- 10 Data Intrinsic Characteristics -- 11 Learning from Imbalanced Data Streams -- 12 Non-Classical Imbalanced Classification Problems -- 13 Imbalanced Classification for Big Data -- 14 Software and Libraries for Imbalanced Classification.
520
$a
This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.
650
0
$a
Machine learning.
$3
561253
650
0
$a
Artificial intelligence
$x
Data processing.
$3
574424
650
0
$a
Big data.
$3
981821
650
1 4
$a
Artificial Intelligence (incl. Robotics)
$3
593924
650
2 4
$a
Information Systems and Communication Service.
$3
669203
650
2 4
$a
Computer Communication Networks.
$3
669310
700
1
$a
Fernandez, Alberto.
$3
1210235
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer eBooks
856
4 0
$u
https://doi.org/10.1007/978-3-319-98074-4
950
$a
Computer Science (Springer-11645)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入