Classification Algorithm

//Classification Algorithm
Classification Algorithm 2018-02-26T21:34:14+00:00

Project Description

Given a set of feature vectors where each vector represents features found in a given column of data, we clustered these columns into a set of discrete domains based on the similarity of the feature vectors. We used Latent Dirichlet Allocation as the clustering technique.

We refined the domain classification algorithm used in step one by considering adjacent domains found in one or more domain graphs. We computed the probability that a column having values ranging from 300 to 800 is a FICO score, given the column is part of a graph that commonly includes a FICO score data element. In other words, P (C1 = FICO-DOMAIN | C1 is part of DG1) is the probability, given that column C1 is part of domain graph DG1, and that DG1 has some probability of including a FICO-DOMAIN.

We designed a method using TensorFlow to combine independently derived domain-graph models, such that the domain and domain graph representations eventually converge on a common set of domains and domain-graphs.

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This Sliding Bar can be switched on or off in theme options, and can take any widget you throw at it or even fill it with your custom HTML Code. Its perfect for grabbing the attention of your viewers. Choose between 1, 2, 3 or 4 columns, set the background color, widget divider color, activate transparency, a top border or fully disable it on desktop and mobile.