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Parallel chi-square test

Parallel chi-square test. Usman Roshan. Chi-square test. The chi-square test is a popular feature selection method when we have categorical data and classification labels as opposed to regression

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Parallel chi-square test

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  1. Parallel chi-square test Usman Roshan

  2. Chi-square test • The chi-square test is a popular feature selection method when we have categorical data and classification labels as opposed to regression • In a feature selection context we would apply the chi-square test to each feature and rank them chi-square values (or p-values) • A parallel solution is to calculate chi-square for all features in parallel at the same time as opposed to one at a time if done serially

  3. Feature=A Feature=B Label=0 Observed=c1 Expected=X1 Observed=c2 Expected=X2 Label=1 Observed=c3 Expected=X3 Observed=c4 Expected=X4 Chi-square test Contingency table • We have two random variables: • Label (L): 0 or 1 • Feature (F): Categorical • Null hypothesis: the two variables are independent of each other (unrelated) • Under independence • P(L,F)= P(D)P(G) • P(L=0) = (c1+c2)/n • P(F=A) = (c1+c3)/n • Expected values • E(X1) = P(L=0)P(F=A)n • We can calculate the chi-square statistic for a given feature and the probability that it is independent of the label (using the p-value). • Features with very small probabilities deviate significantly from the independence assumption and therefore considered important.

  4. Parallel GPU implementation of chi-square test in CUDA • The key here is to organize the data to enable coalescent memory access • We define a kernel function that computes the chi-square value for a given feature • The CUDA architecture automatically distributes the kernel across different GPU cores to be processed simultaneously.

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