Classification Algorithms for Big Data over distributed processing frameworks
Bibliographic record
Abstract
Classification problems have been widely studied in the context of data mining and different approaches to address these problems have been developed in the last decades. Among them, associative classification and decision trees have proved to be very effective and have been successfully employed in several application domains. Furthermore, some of these approaches have integrated the fuzzy set theory with the objective of dealing with uncertain and noise data. Unfortunately, most of the approaches proposed up to now have been designed for maximizing accuracy, often neglecting the complexity both in terms of memory that execution times. Thus, these approaches are generally not able to handle adequately the so-called ``big data''. In this Ph.D. thesis, we propose different solutions in a distributed environment for generating accurate and interpretable classification models for big data. In particular, we focus on associative classification and decision trees, integrating our solutions with fuzzy set theory. Since the generation of such models requires that continuous features are discretized, we also propose a novel distributed discretization approach based on information entropy. This approach has been therefore extended with fuzzy logic for generating fuzzy partitions. Finally, considering the complexity of the models generated by previous solutions, we propose a distributed evolutionary approach for optimizing both accuracy and interpretability of the classifiers. The proposed algorithms are shaped according to the MapReduce programming model and have been deployed on well-known data processing frameworks, widely employed in research as well as industrial contexts. The performance evaluation has been carried out by using different big data benchmarks and the results obtained by the proposed approaches and by some state-of-the-art distributed classification algorithms have been extensively discussed in terms of accuracy, model complexity, and computation time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".