A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

Loyola‐González, Octavio; Medina‐Pérez, Miguel Angel; Choo, Kim‐Kwang Raymond

doi:10.1007/s10723-020-09526-y

Cited by 19 publications

(8 citation statements)

References 189 publications

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“…where v j is a value in the domain of feature f i and # is a relational operator from the set {∈, / ∈, =, ̸ =, ≤, >} [55,57]. For example, [origin corner = R] ∧ [play duration > 10 sec] is a pattern that describes plays starting from the right side of the field, whose duration is greater than ten seconds.…”

Section: Discovery Of Corner Kick Strategiesmentioning

confidence: 99%

“…Contrast pattern mining algorithms can be broadly categorized into exhaustive-searchbased (ESB) algorithms, which execute an exhaustive search of a combination of values for features that are significant in one class in comparison with other classes, and decisiontree-based (DTB) algorithms, which extract contrast patterns from a collection of decision trees [57]. The main drawback of ESB algorithms is that they usually start with an independent a priori discretization of all numeric features [57]; discretizing a numerical attribute without considering the values of other features could hide relations in the objects of a class, causing an important information loss [57,60,61]. Furthermore, mining contrast patterns using ESB algorithms is a challenging problem because of the high computational cost due to the exponential number of candidate patterns [57].…”

Section: Algorithm Selectionmentioning

confidence: 99%

“…The main drawback of ESB algorithms is that they usually start with an independent a priori discretization of all numeric features [57]; discretizing a numerical attribute without considering the values of other features could hide relations in the objects of a class, causing an important information loss [57,60,61]. Furthermore, mining contrast patterns using ESB algorithms is a challenging problem because of the high computational cost due to the exponential number of candidate patterns [57]. Another drawback of ESB algorithms is that they only extract patterns having items in the form [ f i = v j ]; substracting patterns' discriminative power by not using other relational operators, like {∈, / ∈, ̸ =, ≤, >} [57].…”

Section: Algorithm Selectionmentioning

confidence: 99%

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Automated Discovery of Successful Strategies in Association Football

Muñoz,

Monroy,

Cañete-Sifuentes

et al. 2024

Applied Sciences

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Using automated data analysis to understand what makes a play successful in football can enable teams to make data-driven decisions that may enhance their performance throughout the season. Analyzing different types of plays (e.g., corner, penalty, free kicks) requires different considerations. This work focuses on the analysis of corner kick plays. However, the central ideas apply to analyzing all types of plays. While prior analyses (univariate, bivariate, multivariate) have explored the link between contextual factors (e.g., match period, type of defensive marking) and the level of success of a corner kick (e.g., shot, shot on goal, goal), there has been no attempt to combine spatiotemporal event data (sequences of ball movements through the field) and contextual information to determine when and how (strategy) a particular type of corner kick play (tactic) is more likely to succeed or not. To address this gap, we propose an approach that (1) transforms spatiotemporal data into an alternative representation suitable for mining sequential patterns, (2) identifies and characterizes the sequential patterns used by offensive teams to move the ball toward the scoring zone (tactics), and (3) extracts contrast patterns to identify under what conditions different tactics result in increased chances of success or failure; we call these conditions strategies. Our results suggest that favorable and unfavorable conditions for tactic application are not the same across different tactics, supporting the argument that there is a benefit in performing an analysis that treats different tactics separately, where spatiotemporal information plays a crucial role. Unlike prior works on the corner kick, our approach can capture how the interaction between multiple contextual factors impacts the outcome of a corner kick. At the same time, the results can be explained to others in natural languages.

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Section: Discovery Of Corner Kick Strategiesmentioning

confidence: 99%

Section: Algorithm Selectionmentioning

confidence: 99%

Section: Algorithm Selectionmentioning

confidence: 99%

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Automated Discovery of Successful Strategies in Association Football

Muñoz,

Monroy,

Cañete-Sifuentes

et al. 2024

Applied Sciences

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“…A decision path is a path starting from the root node to the leaf node in a single decision tree, and the outcome from the leaf node determines the predicted class C after it goes through the voting process. A logic rule is extracted from a decision path (root to a leaf node) combining the conjunction of the logical test (feature ≤ value) from each node in the path [14,15,48]. Each leaf node generates a unique decision path and leads to different logic rules.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation

2021

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Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of RFMap allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance.

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“…Deep Learning (DL) is a sub-area of AI that has demonstrated outstanding capabilities for solving com-plex tasks in many areas. In computer vision in particular, it has out-performed previous approaches in tasks such as Image Classification, Image Recognition, and Image Segmentation [16,10,26,24,18].…”

Section: Introductionmentioning

confidence: 99%

A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks

Suarez-Ramirez¹,

González-Mendoza²,

Chang-Fernandez³

et al. 2021

LatinX in AI at Computer Vision and Pattern Recognition Conference 2021

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The optimization of Binary Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra requirement of using an approximation to the derivative of the sign function - as it is the Dirac-Delta function - for back-propagation; thus, efforts are focused adapting full-precision techniques to work on BNNs. In the literature, only one previous effort has tackled the problem of directly training the BNNs with bit-flips by using the first raw moment estimate of the gradients and comparing it against a threshold for deciding when to flip a weight (Bop). In this paper, we take an approach parallel to Adam which also uses the second raw moment estimate to normalize the first raw moment before doing the comparison with the threshold, we call this method Bop2ndOrder. We present two versions of the proposed optimizer: a biased one and a bias-corrected one, each with its own applications. Also, we present a complete ablation study of the hyperparameters space, as well as the effect of using schedulers on each of them. For these studies, we tested the optimizer in CIFAR10 using the BinaryNet architecture. Also, we tested it in ImageNet 2012 with the XnorNet and BiRealNet architectures for accuracy. In both datasets our approach proved to converge faster, was robust to changes of the hyperparameters, and achieved better accuracy values.

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A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

Cited by 19 publications

References 189 publications

Automated Discovery of Successful Strategies in Association Football

Automated Discovery of Successful Strategies in Association Football

Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation

A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks

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