Imbalanced Multimodal Attention-Based System for Multiclass House Price Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
House price prediction is an important problem for individuals, companies, organizations, and governments. With a vast amount of diversified and multimodal data available about houses, the predictive models built should seek to make the best use of these data. This leads to the complex problem of how to effectively use multimodal data for house price prediction. Moreover, this is also a context suffering from class imbalance, an issue that cannot be disregarded. In this paper, we propose a new algorithm for addressing these problems: the imbalanced multimodal attention-based system (IMAS). The IMAS makes use of an oversampling strategy that operates on multimodal data, namely using text, numeric, categorical, and boolean data types. A self-attention mechanism is embedded to leverage the usage of neighboring information that can benefit the model’s performance. Moreover, the self-attention mechanism allows for the determination of the features that are the most relevant and adapts the weights used according to that information when performing inference. Our experimental results show the clear advantage of the IMAS, which outperforms all the competitors tested. The analysis of the weights obtained through the self-attention mechanism provides insights into the features’ relevance and also supports the importance of using this mechanism in the predictive model.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 it