Why Is Multiclass Classification Hard?

Moral, Pablo Del; Nowaczyk, Sławomir; Pashami, Sepideh

doi:10.1109/access.2022.3192514

Cited by 24 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing KNN and SVM, we note that using higher k values, as in our case, leads to better performance of KNN [35], whereas the opposite is true for lower k values [26]. Classification accuracies are known to decrease when there are numerous classes to be distinguished [42]. Therefore, the ML models were divided into a three-level classification scheme.…”

Section: A Performance Of Modelsmentioning

confidence: 81%

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

Zahid Rao,

Shahid Siddique,

Danish Mujib

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Advanced assistive devices developed for activities of daily living use machine learning (ML) for motion intention detection using wearable sensors. Trunk assistive devices provide safety, balance, and independence for wheelchair-bound individuals who spend prolonged hours in sitting positions. We used ML for trunk movement intention detection with a trunk orthosis. Sensor fusion technique with four electromyography (EMG) and one inertial measurement unit (IMU) sensor signals are used to develop a three-level classification system. Forty participants engaged in seated trunk movement trials wearing the orthosis. The trials comprised 30 movements involving trunk flexion/extension, lateral bending, and axial rotation. The wrapper method was used to reduce essential EMG features. Ensemble (ES), k-nearest neighbors (KNN), and support vector machine ML classifiers were used. Twenty-six features (five EMG for each of four muscles and six for IMU) were used to develop ten individual ML models, resulting in an average accuracy of 95.44%. Eight models achieved the highest accuracy with the ES and two with KNN. The models were then cascaded to form a trunk motion detection system that achieved a test accuracy of 87.0%. The promising result of this study can be implemented for trunk motion recognition with active trunk orthosis.

show abstract

Section: A Performance Of Modelsmentioning

confidence: 81%

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

Zahid Rao,

Shahid Siddique,

Danish Mujib

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The Multi-Class SVDD (MC-SVDD) * approach here proposed, solves the problem in one shot, without repetitive adaptations and providing the weights for classification as an exact solution to an optimization problem. All uncertainties and data characteristics are handled at the same time, providing a result that best fits the problem [9]. The algorithm generalizes the well-known SVDD by Tax and Duin [10] to the multi-class case, quite naturally as an extension of the original method.…”

Section: A Mc-svddmentioning

confidence: 99%

Multiclass Counterfactual Explanations Using Support Vector Data Description

Carlevaro,

Lenatti,

Paglialonga

et al. 2024

IEEE Trans. Artif. Intell.

View full text Add to dashboard Cite

Explainability has become crucial in Artificial Intelligence studies and, as the complexity of the model increases, so does the complexity of its explanation. However, the higher the complexity of the problem, the higher the amount of information it may provide, and this information can be exploited to generate a more precise explanation of how the model works. One of the most valuable ways to recover such input-output relation is to extract counterfactual explanations that allow us to find minimal changes from an observation to another one belonging to a different class. In this article, we propose a novel methodology to extract multiple counterfactual explanations (MUCH, MUlti Counterfactual via Halton sampling) from an original Multi-Class Support Vector Data Description algorithm. To evaluate the performance of the proposed method, we extracted a set of counterfactual explanations from three state-of-the-art datasets achieving satisfactory results that pave the way to a range of real-world applications.Impact Statement-When a system is analyzed by Artificial Intelligence, the inherent models are posed to the attention of domain experts, thus delegating further possible actions. Counterfactual explanations, on the other hand, directly suggest actuation on the system. Counterfactual control still remains under experts' supervision, but the system improves its level of autonomy. The long-term goal is to make the AI model aware of how to affect the environment properly (both in terms of performance and safety). Examples may include: manoeuvering of autonomous cars, clinical diagnosis, and finance. The proposed approach generalizes counterfactuals intelligibility and control to the multi-class case. The validation over practical scenarios (e.g., the FIFA dataset) corroborates both control precision and quality of counterfactual explanations, thus increasing the readiness level of the approach.

show abstract

“…The approach here proposed Multi-ClassSVDD (MC-SVDD) * , solves the problem in one shot, without repetitive adaptations. All uncertainties and data characteristics are handled at the same time, providing a result that best fits the problem [10]. The algorithm generalizes the well-known SVDD by Tax and Duin [11] to the multi-class case, quite naturally as an extension of the original method.…”

Section: A Mc-svddmentioning

confidence: 99%

Multi-Class Counterfactual Explanations using Support Vector Data Description

Carlevaro¹,

Lenatti²,

Paglialonga³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Explainability is becoming increasingly crucial in machine learning studies and, as the complexity of the model increases, so does the complexity of its explanation. However, the higher the complexity of the problem the higher the amount of information it may provide, and this information can be exploited to generate a more precise explanation of how the model works. One of the most valuable ways to recover such relation between input and output is to extract counterfactual explanations. In binary classification, counterfactuals allow us to find minimal changes from an observation to another one belonging to the opposite class. But how do counterfactuals work in multi-class problems? In this article, we propose a novel methodology to extract multiple counterfactual explanations (MUCH, MUlti Counterfactual via Halton sampling) from an original Multi-Class Support Vector Data Description algorithm (MC-SVDD). To evaluate the performance of the proposed method, we extracted a set of counterfactual explanations from three state-of-the-art datasets achieving satisfactory results that pave the way to a range of real-world applications, for example disease prevention.</p>

show abstract

Why Is Multiclass Classification Hard?

Cited by 24 publications

References 17 publications

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

Multiclass Counterfactual Explanations Using Support Vector Data Description

Multi-Class Counterfactual Explanations using Support Vector Data Description

Contact Info

Product

Resources

About