Parallel Implementations of Chi-Square Test for Feature Selection
The chi-square test is an effective method for feature selection with categorical data and classification labels. It helps rank features based on their chi-square values or p-values, indicating importance. Parallel processing techniques, such as GPU implementation in CUDA, can significantly speed up the computation by evaluating chi-square values for multiple features simultaneously. This approach enhances efficiency in data analysis and model building.
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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 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
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. Feature=A Feature=B Label=0 Observed=c1 Expected=X1 Observed=c2 Expected=X2 Label=1 Observed=c3 Expected=X3 Observed=c4 Expected=X4 (ci- xi)2 xi d-1 c2= i=0
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.
