CJC Experiment Design & Evaluation
Guides rigorous experimental design, baseline evaluation, and empirical reporting for submissions to the Chinese Journal of Computers (CJC).
Designed specifically for researchers submitting full-length papers to the Chinese Journal of Computers (《计算机学报》), this skill provides systematic methodologies for formulating research questions (RQs), selecting fair baselines, and executing comprehensive ablation and robustness studies. It helps authors meet rigorous CCF A-tier peer-review standards by eliminating common pitfalls such as data leakage, unvalidated cherry-picking, and missing statistical significance tests, ensuring experimental evidence precisely matches academic claims.
Key Features
01Establishes fair baseline setups, transparent hyperparameters, and standard benchmarks
02Prevents data leakage and evaluation contamination in machine learning and data mining pipelines
03Enforces statistical rigor with multi-run means, standard deviations, and significance tests
04Structures research question (RQ) driven experimental protocols and evidence mapping
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06Provides guidelines for ablation studies, robustness checks, and failure case analysis
Use Cases
01Structuring the experimental section and research questions for a Chinese Journal of Computers submission
02Conducting ablation studies, overhead analysis, and statistical significance testing for computer science papers
03Auditing evaluation protocols and baseline comparisons prior to CCF A journal peer review