Review - 04/27/2016

[amritbhanu]

Literature Survey

• All the literature survey from SE domain.
• Searched for lda topics stable OR unstable OR coherence on google scholar. Top cited papers.
• 7 out of 9 papers stated topics unstable. So, went for manual validation of topics to do further experiments.
• 1 paper gave a very strong statement that lda is stable. Data not available to verify their results.
• Some other lda toolkit mentioned are GibbsLDA++.
• Some have talked about playing with different configurations. Common Parameters are, k, a, b, i. (BASICALLY TALKING ABOUT TUNING)
• Non deterministic so not many people have bothered about how their results might vary
• Refer this for more details

  DE Experiment

• Implemented DE with CR = 0.3, mutation = 0.7
• Termination criteria, no of iterations.
• Tuning Parameters, k = (10,30), a=(0,1), b=(0,1). Based on literature review.

  Results

• Tuning helped in various datasets for higher no of terms overlap. Or atleast remained same as default parameters.
• No change just by tuning a,b
• Higher k value improved the stability.
• Less b improved the stability.

[amritbhanu]

To Dos:

• [x] Read Papers other than SE domain.
• [x] Verify that tuning really helps for other datasets like SE, SO, Python programming and many more.
• [x] Extend x axis down to 1
• [x] 4 different DEs pop size =10, 30 and reverse CR and Mutation
• [x] Read Weis paper, and verify hypothesis.