메뉴 건너뛰기

?

단축키

Prev이전 문서

Next다음 문서

크게 작게 위로 아래로 댓글로 가기 인쇄 수정 삭제
?

단축키

Prev이전 문서

Next다음 문서

크게 작게 위로 아래로 댓글로 가기 인쇄 수정 삭제
) _ by Rui Nian IntroductionAny real-life data set used for classification is most likely imbalanced, with the event that you are interested in being very rare (minority examples) while non-interesting events dominate the data set (majority examples). Because of this, machine learning models that we build to identify for the rare cases will perform terribly.
An intuitive example: Imagine classifying for credit card fraud. If there are only 5 fraudulent transactions per 1,000,000 transactions, then all our model has to do is predict negative for all data, and the model will be 99.9995% accurate! Thus, the model will most likely learn to "predict negative" no matter what the input data is, and is completely useless! To combat this problem, the data set must be balanced with similar amounts of positive and negative examples.
Some traditional methods to solve this problem are under-sampling and over-sampling. Under-sampling is where the majority class is down sampled to the same amount of data as the minority class. However, this is extremely data inefficient! The discarded data has important information regarding the negative examples.
Imagine building a house cat classifier, and having 1,000,000 images of different species of animals. But only 50 are cat images (positive examples). After down sampling to about 50 negative example images for a balanced data set, we deleted all pictures of tigers and lions in the original data set. Since tigers and lions look similar to house cats, the classifier will mistake them for house cats! We had examples of tigers and lions, but the model was not trained on them because they were deleted! To avoid this problem of data inefficiency, over-sampling is used. In over-sampling, the minority class is copied x times, until its size is similar to the majority class. The greatest flaw here is our model will overfit to the minority data because the same examples appear so many times.
Image from: KaggleTo avoid all of the above problems, ADASYN can be used! ADASYN (Adaptive Synthetic) is an algorithm that generates synthetic data, and its greatest advantages are not copying the same minority data, and generating more data for "harder to learn" examples. How does it work? Let's find out! Throughout the blog, I will also provide the code for each part of the ADASYN algorithm.
The full code can be found here:
A link to the original paper can be found here
ADASYN AlgorithmStep 1Calculate the ratio of minority to majority examples using:
where mₛ and mₗ are the # of minority and majority class examples respectively. If d is lower than a certain threshold, initialize the algorithm.
Step 2Calculate the total number of synthetic minority data to generate.
Here, G is the total number of minority data to generate. ß is the ratio of minority:majority data desired after ADASYN. ß =1 means a perfectly balanced data set after ADASYN.
Step 3Find the k-Nearest Neighbours of each minority example and calculate the rᵢ value. After this step, each minority example should be associated with a different neighbourhood.
The rᵢ value indicates the dominance of the majority class in each specific neighbourhood. Higher rᵢ neighbourhoods contain more majority class examples and are more difficult to learn. See below for a visualization of this step. In the example, K = 5 (looking for the 5 nearest neighbours).
Step 4Normalize the rᵢ values so that the sum of all rᵢ values equals to 1.
This step is mainly a precursor to make step 5 easier.
Step 5Calculate the amount of synthetic examples to generate per neighbourhood.
Because rᵢ is higher for neighbourhoods dominated by majority class examples, more synthetic minority class examples will be generated for those neighbourhoods. Hence, this gives the ADASYN algorithm its adaptive nature; more data is generated for "harder-to-learn" neighbourhoods.
Step 6Generate Gᵢ data for each neighbourhood. First, take the minority example for the neighbourhood, xᵢ. Then, randomly select another minority example within that neighbourhood, xzᵢ. The new synthetic example can be calculated using:
In the above equation, λ is a random number between 0–1, sᵢ is the new synthetic example, xᵢ and xzᵢ are two minority examples within a same neighbourhood. A visualization of this step is provided below. Intuitively, synthetic examples are created based on a linear combination of xᵢ and xzᵢ.
White noise can be added to the synthetic examples to make the new data even more realistic. Also, instead of linear interpolation, planes can be drawn between 3 minority examples, and points can be generated on the plane instead.
And that's it! With the above steps, any imbalanced data set can now be fixed, and the models built using the new data set should be much more effective.
Weaknesses to ADASYNThere are two major weaknesses of ADASYN:
For minority examples that are sparsely distributed, each neighbourhood may only contain 1 minority example.Precision of ADASYN may suffer due to adaptability nature.To solve the first issue, neighbourhoods with only 1 minority example can have its value duplicated Gi times. A second way is to simply ignore producing synthetic data for such neighbourhoods. Lastly, we can also increase the neighbourhood size.
The second issue arises because more data is generated in neighbourhoods with high amounts of majority class examples. Because of this, the synthetic data generated might be very similar to the majority class data, potentially generating many false positives. One solution is to cap Gi to a maximum amount, so not too many examples are made for these neighbourhoods.
ConclusionThat wraps up the ADASYN algorithm. The biggest advantages of ADASYN are it's adaptive nature of creating more data for "harder-to-learn" examples and allowing you to sample more negative data for your model. Using ADASYN, you can ultimately synthetically balance your data set!
The full code is available on my GitHub:
Thanks for reading, let me know if you have any questions on comments!
Machine LearningData ScienceClassificationAdasynSynthetic Data--
--
7
FollowWritten by Rui Nian45 FollowersAdvanced Process Control Engineer — focused on ML for prediction, monitoring, and control
FollowHelp
Status
About
Careers
Press
Blog
Privacy
Terms
Text to speech
Teams

If you liked this article so you would like to acquire more info relating to Woodworking please visit our own webpage.

List of Articles
번호 제목 글쓴이 날짜 조회 수
11374 Guide Pour L'achat De Devise étrangère Au Canada RosauraX3484630290 2024.12.10 1
11373 Plainte à La Commission Scolaire : Comment Faire Valoir Vos Préoccupations JacquesVzr26513 2024.12.10 2
11372 VersusBola Tempat Terbaik Bagi Menang Besar Pada Judi Bola Dan Slot Machine Online! NormaDenny89027196 2024.12.10 11
11371 VersusBola Menang Bersama Dalam Situs Judi Bola Dan Slot Gacor Terbaik! FredricWills9628 2024.12.10 3
11370 Land For Sale ForrestMcMillen36 2024.12.10 1
11369 Bet777 Casino Review SallieLeibowitz 2024.12.10 1
11368 Mejores Tiendas Online Para Comprar Camisetas De Hull City Económicas MarioLarocque52861 2024.12.10 1
11367 Acheter Des Francs Suisses Sur Le Canada : Guide Pratique ShanonMotsinger84870 2024.12.10 1
11366 Believing These Four Myths About Chaturbate Token Purchase Keeps You From Growing EltonHipple7793 2024.12.10 1
11365 Leading Free Live Cam Chat Services For Fun And Interaction NRXJennie1961368 2024.12.10 7
11364 The Benefits Of Webcam Chatting KateBurkholder441878 2024.12.10 1
11363 Condos à Vendre à Gatineau : Trouvez Votre Nouveau Foyer ZOVMikayla90154339206 2024.12.10 1
11362 Leading Random Chat Sites For Exciting Conversations InaODea33432609745 2024.12.10 1
11361 Best Adult Video Chat Apps To Explore EllenWooldridge 2024.12.10 1
11360 Guide Par Acheter Des Euros Sur Le Canada LizetteConolly3941 2024.12.10 1
11359 Best Online Cam Chat Services Available JanineWeatherburn55 2024.12.10 1
11358 Empresa De Tratamiento De Humedades RuebenHollenbeck 2024.12.10 1
11357 Leading Adult Video Chat Platforms You Should Know About JaclynGir53798718 2024.12.10 1
11356 Échange De Devises En Institution Financière Au Canada : Ce Que Vous Devez Savoir CandidaSpangler8 2024.12.10 1
11355 Land For Sale DillonTilley73629 2024.12.10 1
Board Pagination Prev 1 ... 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 ... 1635 Next
/ 1635

BANKING ACCOUNT

예금주: 한빛사무기(반재현)

신한은행 100-031-495955

CUSTOMER CENTER

고객센터: 1688-5386

고객센터: 010-5485-8060

팩스: 043-277-7130

이메일: seoknamkang@gmail.com

업무시간: 평일 08-18시. 토, 공휴일휴무

주소: 청주시 흥덕구 복대로 102 세원아파트상가 2층 (복대동 세원아프트 단지내 슈퍼 옆)

대표: 강석남

사업자등록번호: 301-31-50538

통신판매업 신고번호: 012-12345-123

© k2s0o1d4e0s2i1g5n. All Rights Reserved