This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Specifically, in the modern era of massive datacollections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. One type of implementation of a content strategy that is specific to datacollections are data catalogs. Data catalogs are very useful and important.
This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD). DataCollection.
Insufficient training data in the minority class — In domains where datacollection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. Data mining for direct marketing: Problems and solutions. Protein classification with imbalanced data. References. 30(2–3), 195–215.
The surrogate model is often a simple linear model or a decision tree, which are innately interpretable, so the datacollected from the perturbations and the corresponding class output can provide a good indication on what influences the model’s decision. Conference on KnowledgeDiscovery and Data Mining, pp.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content