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Adaboost extensions for cost-sentive classification
Adaboost extensions for cost-sentive classification
CSExtension 1
CSExtension 2
CSExtension 3
CSExtension 4
CSExtension 5
AdaCost
Boost
CostBoost
Uboost
CostUBoost
AdaBoostM1
Implementation of all the listed algorithms of the cluster "cost-sensitive classification".
They are the meta algorithms which requires base algorithms e.g. Decision Tree
Moreover,
Voting criteria is also required e.g. Minimum expected cost criteria
Input also requires to load an arff file and a cost matrix (sample arff and cost files are uploaded for users' reference)
This extension uses weka for classification and generates the classification model along with confusion matrix. For given dataset and cost matrix
This .NET/Mono library provides some utilities for handling tree structures. Most importantly, it supports the display of tree structures as two-dimensional graphics. By using ready-made interfaces, you can specify the size of your nodes, and the Tree Layout Helper library will take care of placing those nodes appropriately.
Free Open Source VoiceXML editor programmed in Java (Swing). The VoiceXML document is regularly parsed, a tree view is built and syntax errors are reported in a specific table.
Atera all-in-one platform IT management software with AI agents
Ideal for internal IT departments or managed service providers (MSPs)
Atera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
A flexible Japanese to English language translation engine that allows multiple results be displayed according to all possible grammar ratings, i.e. a list of results will be displayed instead of only one. Please browse the CVS tree for actual project.
A ContextTree is a way of dynamically forming relationships between information: the same information can be viewed in different ways, depending on what you want from it.
The main idea behind this project is the use of a common text to enclose a message by manipulating the structure of the semantic tree of each phrase in the text.