Subspace selection in high-dimensional big data using genetic algorithm in apache spark
Bibliographic record
Abstract
In high-dimensional space with large amounts of data, distances between data points tend to become relatively uniform. The notion of the nearest neighbours of a data point thus becomes meaningless, a phenomenon known as "curse of dimensionality." Identifying outliers (data points with statistical characteristics significantly different than the majority of the data) in such a high-dimensional space can be a significant challenge. Mining for outliers in subspaces with relevant attributes is one of approaches for this problem, and identifying these attributes is the main objective of this work. In this paper, we scale a grid-based solution to search for subspaces that are candidates for outlier detection with regard to the subset of features in the subspace. We specify a population and a fitness function for a distributed genetic algorithm to heuristically search the subspaces within the high dimensional data, and find the subspace with maximal sparsity. We designed and implemented our proposed subspace selection algorithm in Apache Spark, a fast in-memory engine for large-scale data processing. The initial experimental results on a large dataset (77,000 records and 1,379 attributes) confirm that our proposed method can identify the most relevant subspaces for outlier detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".