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REFERENCE
[1] M. G. P. Garcia, M. C. Medeiros, and G. F. R. Vasconcelos, “Real-time inflation forecasting with high-dimensional
models: The case of Brazil,” Int. J. Forecast., vol. 33, no. 3, pp. 679–693, Jul. 2017, doi: 10.1016/j.ijforecast.2017.02.002.
[2] J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A Review on Generative Adversarial Networks: Algorithms, Theory, and
Applications,” IEEE Trans. Knowl. Data Eng., 2021, doi: 10.1109/TKDE.2021.3130191.
[3] P. Orban et al., “Multisite generalizability of schizophrenia diagnosis classification based on functional brain
connectivity,” Schizophr. Res., vol. 192, pp. 167–171, Feb. 2018, doi: 10.1016/j.schres.2017.05.027.
[4] V. Feldman, A. McMillan, and K. Talwar, “Hiding among the Clones: A Simple and Nearly Optimal Analysis of Privacy
Amplification by Shuffling,” in Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, IEEE Computer
Society, 2022, pp. 954–964. doi: 10.1109/FOCS52979.2021.00096.
[5] G. S. Kashyap, K. Malik, S. Wazir, and R. Khan, “Using Machine Learning to Quantify the Multimedia Risk Due to
Fuzzing,” Multimed. Tools Appl., vol. 81, no. 25, pp. 36685–36698, Oct. 2022, doi: 10.1007/s11042-021-11558-9.
[6] D. Forsberg, E. Sjöblom, and J. L. Sunshine, “Detection and Labeling of Vertebrae in MR Images Using Deep Learning
with Clinical Annotations as Training Data,” J. Digit. Imaging, vol. 30, no. 4, pp. 406–412, Aug. 2017, doi: 10.1007/s10278-017-9945-
x.
[7] X. E. Pantazi, D. Moshou, T. Alexandridis, R. L. Whetton, and A. M. Mouazen, “Wheat yield prediction using machine
learning and advanced sensing techniques,” Comput. Electron. Agric., vol. 121, pp. 57–65, 2016, doi: 10.1016/j.compag.2015.11.018.
[8] X. Zhong and D. Enke, “Predicting the daily return direction of the stock market using hybrid machine learning
algorithms,” Financ. Innov., vol. 5, no. 1, pp. 1–20, Dec. 2019, doi: 10.1186/s40854-019-0138-0.
[9] I. Eskinazi and B. J. Fregly, “Surrogate modeling of deformable joint contact using artificial neural networks,” Med.
Eng. Phys., vol. 37, no. 9, pp. 885–891, Sep. 2015, doi: 10.1016/j.medengphy.2015.06.006.
[10] Y. Jiang and C. Li, “MRMR-based feature selection for classification of cotton foreign matter using hyperspectral
imaging,” Comput. Electron. Agric., vol. 119, pp. 191–200, Nov. 2015, doi: 10.1016/j.compag.2015.10.017](https://image.slidesharecdn.com/teeconfinalpresentation-250418174613-d0e9fbed/85/Visualizing-Machine-Learning-Models-for-Enhanced-Financial-Decision-Making-and-Risk-Management-9-320.jpg)
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Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management
- 1. 1 TEECCON 2024 Authors Details VisualizingMachine Learning Models for Enhanced Financial Decision- Making and Risk Management Priyam Ganguly Ramakrishna Garine Isha Mukherjee Widener University University of North Texas Pace University
- 2. 2 Contents ● Abstract ● Introduction ●Related words ● Methodology ● Experimental analysis ● Conclusion and Future work ● Reference
- 3. 3 ABSTRACT Importance of ModelVisualization: Visualizing machine learning models is crucial for improving interpretability, especially in the banking sector, where understanding predictions in high-stakes financial contexts is essential. Performance and Innovation Support: Visual tools enhance model performance and foster the development of innovative financial models by offering deeper insights into algorithmic decision-making processes. Improving Financial Concepts Understanding: Through visually guided experiments, complex concepts like risk assessment and portfolio allocation become more accessible and understandable within financial machine learning frameworks. Risk Appetite and Trading Strategy Correlation: The study finds that risk tolerance and trading strategies are linked, showing a negative correlation between portfolio rebalancing frequency and risk tolerance. New Visualization-Driven Method for Asset Weighting: A novel approach to locally stochastic asset weighting is introduced, where visualization aids in data extraction and validation, underscoring the method's value in advancing financial machine learning research.
- 4. 4 INTRODUCTION Challenges of ComplexML Algorithms: The rapid advancement in ML has produced complex algorithms that often lack interpretability, raising ethical concerns in high-stakes fields like medicine and law, where transparency in decision-making is essential. Role of Visualization for Interpretability: Visualization techniques help elucidate decision-making processes in complex ML models (e.g., ANNs, NLP algorithms), enhancing trust and reliability, particularly in fields with significant consequences for errors. Performance Gap Between Training and Real-World Data: ML models frequently exhibit performance discrepancies when tested on real-world data versus training data, necessitating visual analysis to understand internal dynamics and bridge this performance gap. Tsetlin Machine (TM) as a Transparent Alternative: Unlike traditional neural networks, the TM uses propositional logic for supervised learning, offering interpretability through clear decision-making processes, making it a promising option for applications where transparency is vital. Detailed Analysis of TM Learning Dynamics: The study investigates TM's internal mechanisms, including TA state flips and clause computation, by adjusting hyperparameters (like 's' and 'T') and measuring their influence on learning. This analysis aims to enhance understanding of TM dynamics and optimize model accuracy.
- 5. 5 RELATED WORDS Comparison ofTsetlin Machines (TM) and Artificial Neural Networks (ANNs): While ANNs are foundational in ML, the TM has emerged as a competitive, energy-efficient alternative with inherent interpretability, making it suitable for high-stakes fields like healthcare and law. Addressing the Accuracy-Interpretability Trade-off: The study highlights the importance of interpretability in ML, especially in complex applications, and examines two main approaches—intrinsic and post-hoc interpretability—to enhance understanding of model decisions. Enhanced Clause Discrimination with Weighted TM Variants: Innovations like the Weighted Tsetlin Machine (WTM) and Integer Weighted Tsetlin Machine (IWTM) improve TM accuracy and interpretability by optimizing clause weights and reducing unnecessary literals. Locally Random Clause Removal to Prevent Overfitting: By selectively removing underperforming clauses, the study aims to mitigate overfitting in TM, thereby boosting model efficiency and interpretability. Real-Time Visualization of TM Learning Dynamics: The study proposes developing visualization tools to track clause formation and state transitions during training, providing insights into the TM’s decision-making processes for improved transparency.
- 6. 6 METHODOLOGY Boolean Input Processingin Tsetlin Machines (TM): TMs process Boolean input vectors, transforming each feature into a set of literals, including both original and negated forms. This dual representation enables the TM to capture more complex patterns by including both positive and negative contributions in clause formation. Role of Tsetlin Automaton (TA) for Literal Selection: Each literal in a TM is associated with a TA that determines whether the literal is included or excluded based on its relevance. This dynamic allocation addresses the multi-armed bandit problem by rewarding or penalizing states to maximize useful literals for each clause. Clause Construction Using Boolean Logic: The TM constructs clauses by ANDing included literals and negated literals. Class Voting Mechanism: Clauses are divided into positive and negative polarities, contributing either in favor of or against a class label. The TM sums the outputs from positive and negative clauses, applying a threshold to determine the final class prediction based on the combined confidence score. Boolean Algebra for Literal Inclusion/Exclusion: The inclusion or exclusion of each literal is implemented using Boolean operations that involve the literal’s TA state, ensuring that any excluded literals result in a clause output of 0. This approach allows the TM to efficiently filter out irrelevant information while maintaining computational simplicity.
- 7. 7 EXPERIMENTAL ANALYSIS Objective andTool Development: The study aimed to create a tool that trains a clause-based Threshold Model (TM) on the Noisy XOR dataset, generates clauses from training, and visualizes the decision-making process for new inputs, focusing on the interpretability of TM and the impact of clause voting on predictions. Dataset and Model Performance: The TM was trained on the Noisy XOR dataset, which comprised 5000 inputs with 12 features and a signal-to-noise ratio of 69.8%. The model achieved 100% accuracy, demonstrating its effectiveness in handling the dataset, with a structure of 10 clauses per class divided into positive and negative polarities. Visualization of Clause Contributions: A Python script was developed to visualize the TM's voting process on unseen inputs, illustrating how each clause contributed to final class votes. The results indicated the TM’s voting behavior for specific input arrays, confirming the successful implementation of clause summation and class voting. Hyperparameter Analysis: The study examined the effects of two key hyperparameters, learning sensitivity ('s') and thresholding ('T'), on the TM’s performance. Lower values of 's' (e.g., 4.0) resulted in significantly lower average number of TA flips (ANOF) and optimal accuracy (90.9%), while higher values led to increased ANOF and decreased accuracy. Impact of Learning Sensitivity on Stability: It was found that as the learning sensitivity 's' increased, the TAs (threshold attributes) became more stable, exhibiting fewer state transitions. The rate of change in ANOF was inversely proportional to 's', indicating a trade-off between sensitivity and the stability of the model during training.
- 8. 8 CONCLUSIONS AND FUTUREWORKS Focus on Visualization and Interpretability: This study rigorously explores the visualization of machine learning (ML), particularly the implementation of Threshold Models (TMs), highlighting the essential role of interpretability in understanding ML theories and enhancing the performance of low-complexity architectures. Insights into TM Theory: The research provides foundational insights into the theoretical framework of the TM, demonstrating how visualization efforts reveal a significant relationship between the learning rate and the frequency of TA (threshold attribute) flips, thereby enhancing comprehension of the model's behavior. Importance of Visualization Tools: The findings underscore the value of visualization tools in elucidating the inner workings of the TM, facilitating a deeper understanding of how various parameters impact model performance and decision-making processes. Achievement of Study Objectives: The study has largely met its objectives, successfully integrating visualization techniques with TM implementation to provide meaningful insights into the model's interpretability and operational dynamics. Future Research Directions: The conclusion advocates for the implementation of locally stochastic clause dropping as an innovative approach, suggesting that further exploration of its effects on clause redundancy could represent a promising direction for future research in the field of machine learning.
- 9. 9 REFERENCE [1] M. G.P. Garcia, M. C. Medeiros, and G. F. R. Vasconcelos, “Real-time inflation forecasting with high-dimensional models: The case of Brazil,” Int. J. Forecast., vol. 33, no. 3, pp. 679–693, Jul. 2017, doi: 10.1016/j.ijforecast.2017.02.002. [2] J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications,” IEEE Trans. Knowl. Data Eng., 2021, doi: 10.1109/TKDE.2021.3130191. [3] P. Orban et al., “Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity,” Schizophr. Res., vol. 192, pp. 167–171, Feb. 2018, doi: 10.1016/j.schres.2017.05.027. [4] V. Feldman, A. McMillan, and K. Talwar, “Hiding among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling,” in Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, IEEE Computer Society, 2022, pp. 954–964. doi: 10.1109/FOCS52979.2021.00096. [5] G. S. Kashyap, K. Malik, S. Wazir, and R. Khan, “Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing,” Multimed. Tools Appl., vol. 81, no. 25, pp. 36685–36698, Oct. 2022, doi: 10.1007/s11042-021-11558-9. [6] D. Forsberg, E. Sjöblom, and J. L. Sunshine, “Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data,” J. Digit. Imaging, vol. 30, no. 4, pp. 406–412, Aug. 2017, doi: 10.1007/s10278-017-9945- x. [7] X. E. Pantazi, D. Moshou, T. Alexandridis, R. L. Whetton, and A. M. Mouazen, “Wheat yield prediction using machine learning and advanced sensing techniques,” Comput. Electron. Agric., vol. 121, pp. 57–65, 2016, doi: 10.1016/j.compag.2015.11.018. [8] X. Zhong and D. Enke, “Predicting the daily return direction of the stock market using hybrid machine learning algorithms,” Financ. Innov., vol. 5, no. 1, pp. 1–20, Dec. 2019, doi: 10.1186/s40854-019-0138-0. [9] I. Eskinazi and B. J. Fregly, “Surrogate modeling of deformable joint contact using artificial neural networks,” Med. Eng. Phys., vol. 37, no. 9, pp. 885–891, Sep. 2015, doi: 10.1016/j.medengphy.2015.06.006. [10] Y. Jiang and C. Li, “MRMR-based feature selection for classification of cotton foreign matter using hyperspectral imaging,” Comput. Electron. Agric., vol. 119, pp. 191–200, Nov. 2015, doi: 10.1016/j.compag.2015.10.017
- 10.
Editor's Notes
- #1 Good [morning/afternoon], everyone! It’s a pleasure to be here today to discuss our research: Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management .My name is Ramakrishna Garine and I am from University of North Texas. Today, we’ll walk you through how we leveraged machine learning visualization techniques to address critical challenges in financial decision-making. Let’s begin!
- #3 Visualization plays a crucial role in machine learning, particularly in high-stakes fields like finance. Here, decisions often involve significant risks, so it’s vital that stakeholders understand not just the results but the reasoning behind them. Our study focuses on improving the interpretability of machine learning models. Specifically, we’ve introduced visual tools that enhance understanding of complex concepts like risk assessment and portfolio allocation. These tools don’t just improve decision-making—they foster innovation by making it easier to develop and validate new financial models. One key insight from our research is the correlation between risk tolerance and trading strategies. We found that more frequent portfolio rebalancing is associated with lower risk tolerance. Finally, we’ve developed a novel, visualization-driven method for locally stochastic asset weighting. This approach helps extract and validate data more effectively, pushing the boundaries of financial machine learning research.
- #4 The rapid growth of machine learning has brought about incredibly powerful algorithms. However, with this power comes complexity, and often, these models operate as ‘black boxes’—meaning their decision-making processes are opaque. This lack of transparency raises ethical concerns, especially in fields like medicine, law, and finance, where the consequences of errors can be severe. Take artificial neural networks, for instance. They are highly effective at pattern recognition but offer little insight into why they make specific predictions. This is where visualization becomes essential. By visually representing the inner workings of these models, we can better understand their decisions, improve trust, and ensure their reliability. Our research explores the Tsetlin Machine, a unique model that offers inherent interpretability through its use of propositional logic. We’ll demonstrate how it can be a powerful alternative to traditional neural networks, particularly in financial applications.”
- #5 Let’s take a moment to discuss how our work fits within the broader landscape of machine learning research.”“Artificial Neural Networks, or ANNs, have been instrumental in advancing machine learning. However, their black-box nature makes them less ideal for applications that require transparency.”“In contrast, the Tsetlin Machine is an energy-efficient model that provides interpretability by using rule-based logic. Our research builds on existing innovations like the Weighted Tsetlin Machine (WTM) and the Integer Weighted Tsetlin Machine (IWTM), which optimize clause selection to improve both accuracy and interpretability. Another key area we explore is overfitting, which occurs when models capture noise or irrelevant patterns. To address this, we propose a method for locally random clause removal, enhancing the model’s efficiency without sacrificing accuracy. Finally, we aim to go beyond the static analysis of model behavior by introducing real-time visualization tools that track the learning dynamics of Tsetlin Machines.
- #6 Now, let’s dive into our methodology, starting with how the Tsetlin Machine operates. Tsetlin Machines process data in the form of Boolean vectors. This means each feature is represented as both its original and negated form, allowing the model to capture complex patterns more effectively. The Tsetlin Automaton is a key component here. It dynamically decides whether to include or exclude specific literals in a clause based on their relevance. Once the clauses are formed, they’re divided into positive and negative categories. These clauses vote for or against specific class labels, and the final prediction is based on the sum of these votes. This entire process relies on Boolean logic, which ensures computational efficiency while maintaining transparency.
- #7 The experimental phase of our study focused on the Noisy XOR dataset, a benchmark dataset with 5,000 inputs and a 69.8% signal-to-noise ratio. Our goal was to create a tool that could visualize the Tsetlin Machine’s decision-making process. After training the model, we used this tool to analyze how each clause contributed to predictions on new, unseen data. The results were impressive: the model achieved 100% accuracy on the Noisy XOR dataset, demonstrating its effectiveness even under noisy conditions. We then used Python scripts to visualize clause contributions. This allowed us to see how positive and negative clauses influenced the final class vote. These visualizations provided valuable insights into the model’s behavior, confirming the successful implementation of our interpretability framework. An important part of our research was understanding how hyperparameters affect the Tsetlin Machine’s performance.We focused on two key parameters: learning sensitivity (‘s’) and thresholding (‘T’). First, we varied the sensitivity parameter. Lower values of ‘s’ led to fewer state transitions, which improved the model’s stability and accuracy. For instance, with ‘s’ set to 4.0, we achieved an accuracy of 90.9%.”“Next, we looked at the thresholding parameter. As we increased the threshold, the model required fewer updates, which reduced the average number of Tsetlin Automaton flips. These experiments highlight the importance of fine-tuning hyperparameters to balance accuracy, efficiency, and stability.
- #8 To wrap up, our study underscores the vital role of visualization in machine learning. By focusing on the Tsetlin Machine, we’ve demonstrated how interpretability can be integrated into powerful, yet transparent, financial models. Our visual tools not only enhance understanding but also improve the model’s overall performance by revealing hidden dynamics. Looking ahead, we’re excited about the potential of locally stochastic clause dropping, a technique that could further optimize model efficiency. We believe these advancements will inspire further research in visualizing machine learning, especially in critical fields like finance.
- #9 Lastly, we’d like to acknowledge the various studies and researchers whose work has informed our research. We’ve drawn on a wide range of literature to ensure our approach is both innovative and well-grounded in existing research. If you’d like more details about any of these references, feel free to reach out to us.