This work investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object. Five types of input elicitation methods are tested: binary classification (positive or negative); the ( x, y )-coordinate of the position participants believe a target object is located; level of confidence in binary response (on a scale from 0 to 100%); what participants believe the majority of the other participants' binary classification is; and participant's perceived difficulty level of the task (on a discrete scale). We design two crowdsourcing studies to test the performance of a variety of input elicitation methods and utilize data from over 300 participants. Various existing voting and machine learning (ML) methods are applied to make the best use of these inputs. In an effort to assess their performance on classification tasks of varying difficulty, a systematic synthetic image generation process is developed. Each generated image combines items from the MPEG-7 Core Experiment CE-Shape-1 Test Set into a single image using multiple parameters (e.g., density, transparency, etc.) and may or may not contain a target object. The difficulty of these images is validated by the performance of an automated image classification method. Experiment results suggest that more accurate results can be achieved with smaller training datasets when both the crowdsourced binary classification labels and the average of the self-reported confidence values in these labels are used as features for the ML classifiers. Moreover, when a relatively larger properly annotated dataset is available, in some cases augmenting these ML algorithms with the results (i.e., probability of outcome) from an automated classifier can achieve even higher performance than what can be obtained by using any one of the individual classifiers. Lastly, supplementary analysis of the collected data demonstrates that other performance metrics of interest, namely reduced false-negative rates, can be prioritized through special modifications of the proposed aggregation methods.
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Noise in Datasets: What Are the Impacts on Classification Performance? [Noise in Datasets: What Are the Impacts on Classification Performance?]
Classification is one of the fundamental tasks in machine learning. The quality of data is important in con-
structing any machine learning model with good prediction performance. Real-world data often suffer from
noise which is usually referred to as errors, irregularities, and corruptions in a dataset. However, we have no
control over the quality of data used in classification tasks. The presence of noise in a dataset poses three major
negative consequences, viz. (i) a decrease in the classification accuracy (ii) an increase in the complexity of
the induced classifier (iii) an increase in the training time. Therefore, it is important to systematically explore
the effects of noise in classification performance. Even though there have been published studies on the effect
of noise either for some particular learner or for some particular noise type, there is a lack of study where the
impact of different noise on different learners has been investigated. In this work, we focus on both scenar-
ios: various learners and various noise types and provide a detailed analysis of their effects on the prediction
performance. We use five different classifiers (J48, Naive Bayes, Support Vector Machine, k-Nearest Neigh-
bor, Random Forest) and 10 benchmark datasets from the UCI machine learning repository and three publicly
available image datasets. Our results can be used to guide the development of noise handling mechanisms.
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- Award ID(s):
- 1946231
- NSF-PAR ID:
- 10319530
- Date Published:
- Journal Name:
- Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.5220/0010782200003122
