What is entropy based discretization?
An entropy-based discretization method for classification rules with inconsistency checking. So the interaction between all attributes is taken into consideration in the discretization process which makes our method possess a global property. Experimental results indicate that with the same rule generator C4.
What is entropy based binning?
Entropy-based Binning Entropy based method uses a split approach. The entropy (or the information content) is calculated based on the class label. Intuitively, it finds the best split so that the bins are as pure as possible that is the majority of the values in a bin correspond to have the same class label.
What is entropy based?
Alois Pichler, Ruben Schlotter. Entropy is a measure of self-information which is used to quantify losses. Entropy was developed in thermodynamics, but is also used to compare probabilities based on their deviating information content.
Can we use entropy for data discretization?
Entropy is a fundamental concept in Data Mining that is used far beyond simple discretization of data. These approaches are also used for decision trees and rule-based classifiers, so understanding it is definitely a useful tool to have in your toolbelt. That’s all for now!
Is binning supervised or unsupervised?
We compare binning, an unsupervised discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm signi cantly improved when features were discretized us- ing an entropy-based method.
What is entropy MDL?
Entropy-MDL, invented by Fayyad and Irani is a top-down discretization, which recursively splits the attribute at a cut maximizing information gain, until the gain is lower than the minimal description length of the cut. The widget can also be set to leave the attributes continuous or to remove them.
What is entropy in stock market?
Entropy refers to the degree of randomness or uncertainty pertaining to a market or security. Entropy is used by analysts and market technicians to describe the level of error that can be expected for a particular prediction or strategy.
What is entropy detection?
The entropy detection method is an effective method to detect the DDoS attack. It is mainly used to calculate the distribution randomness of some attributes in the network packets’ headers. Experiment results show that these methods could lead to more accurate and effective DDoS detection.
What are the types of main discretization techniques?
Of course, on top of those things I just mentioned, there are basically three different type of discretization techniques in numerical methods: finite difference, finite element, and finite volume, as explained by previous answers.
What is supervised binning?
Supervised binning is a type of binning that transforms a numerical or continuous variable into a categorical variable considering the target class label into account. It refers to the target class label when selecting discretization cut points. Entropy-based binning is a type of supervised binning.
What is entropy-based method in discretization?
Entropy based method is one of discretization methods however using information entropy measure. In this paper, the aim was to use the entropy-based method in the discretization with a proposed algorithm. This algorithm attempts to find suitable cut-points through new concepts.
What is entropy in data mining?
Entropy is a fundamental concept in Data Mining that is used far beyond simple discretization of data. These approaches are also used for decision trees and rule-based classifiers, so understanding it is definitely a useful tool to have in your toolbelt.
What is discretization in data mining?
Discretization is a common process used in data mining applications that transforms quantitative data into qualitative data. Different methods have been proposed in order to achieve this process. The stand stone in the discretization algorithm is to find potential cut-points which split continuous range values into nominal values.
What is the best value of entropy for age based income?
Lower entropy is better, and a 0 value for entropy is the best. For example, of 10 people below 25, let’s say that we have 6 that make above 50K each year and 4 that make below 50K each year. It looks something like this: This would have a high entropy value (closer to 1). If we’re trying to determine income based on age, we ask ourselves: