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Mustafa Bilgic

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6 papers

1 author row

IJCAI Conference 2023 Conference Paper

Context-Aware Feature Selection and Classification

Juanyan Wang
Mustafa Bilgic

We propose a joint model that performs instance-level feature selection and classification. For a given case, the joint model first skims the full feature vector, decides which features are relevant for that case, and makes a classification decision using only the selected features, resulting in compact, interpretable, and case-specific classification decisions. Because the selected features depend on the case at hand, we refer to this approach as context-aware feature selection and classification. The model can be trained on instances that are annotated by experts with both class labels and instance-level feature selections, so it can select instance-level features that humans would use. Experiments on several datasets demonstrate that the proposed model outperforms eight baselines on a combined classification and feature selection measure, and is able to better emulate the ground-truth instance-level feature selections. The supplementary materials are available at https: //github. com/IIT-ML/IJCAI23-CFSC.

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TMLR Journal 2023 Journal Article

The Analysis of the Expected Change in the Classification Probability of the Predicted Label

Ruo Yang
Ping Liu
Mustafa Bilgic

We present a formalism for estimating the expected change in the probability distribution of the predicted label of an object, with respect to all small perturbations to the object. We first derive analytically an estimate of the expected probability change as a function of the input noise. We then conduct three empirical studies: in the first study, experimental results on image classification show that the proposed measure can be used to distinguish the not-robust label predictions from those that are robust, even when they are all predicted with high confidence. The second study shows that the proposed robustness measure is almost always higher for the predictions on the corrupted images, compared to the predictions on the original versions of them. The final study shows that the proposed measure is lower for models when they are trained using adversarial training approaches.

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AAAI Conference 2021 Conference Paper

Relational Classification of Biological Cells in Microscopy Images

Ping Liu
Mustafa Bilgic

We investigate the relational classification of biological cells in 2D microscopy images. Rather than treating each cell image independently, we investigate whether and how the neighborhood information of a cell can be informative for its prediction. We propose a Relational Long Short-Term Memory (R-LSTM) algorithm, coupled with auto-encoders and convolutional neural networks, that can learn from both annotated and unlabeled microscopy images and that can utilize both the local and neighborhood information to perform an improved classification of biological cells. Experimental results on both synthetic and real datasets show that R-LSTM performs comparable to or better than six baselines.

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AAAI Conference 2014 Conference Paper

Anytime Active Learning

Maria Ramirez-Loaiza
Aron Culotta
Mustafa Bilgic

A common bottleneck in deploying supervised learning systems is collecting human-annotated examples. In many domains, annotators form an opinion about the label of an example incrementally — e. g. , each additional word read from a document or each additional minute spent inspecting a video helps inform the annotation. In this paper, we investigate whether we can train learning systems more efficiently by requesting an annotation before inspection is fully complete — e. g. , after reading only 25 words of a document. While doing so may reduce the overall annotation time, it also introduces the risk that the annotator might not be able to provide a label if interrupted too early. We propose an anytime active learning approach that optimizes the annotation time and response rate simultaneously. We conduct user studies on two document classification datasets and develop simulated annotators that mimic the users. Our simulated experiments show that anytime active learning outperforms several baselines on these two datasets. For example, with an annotation budget of one hour, training a classifier by annotating the first 25 words of each document reduces classification error by 17% over annotating the first 100 words of each document.

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AAAI Conference 2010 Conference Paper

Active Inference for Collective Classification

Mustafa Bilgic
Lise Getoor

Labeling nodes in a network is an important problem that has seen a growing interest. A number of methods that exploit both local and relational information have been developed for this task. Acquiring the labels for a few nodes at inference time can greatly improve the accuracy, however the question of figuring out which node labels to acquire is challenging. Previous approaches have been based on simple structural properties. Here, we present a novel technique, which we refer to as reflect and correct, that can learn and predict when the underlying classification system is likely to make mistakes and it suggests acquisitions to correct those mistakes.

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AAAI Conference 2007 Conference Paper

VOILA: Efficient Feature-value Acquisition for Classification

Mustafa Bilgic

We address the problem of efﬁcient feature-value acquisition for classiﬁcation in domains in which there are varying costs associated with both feature acquisition and misclassi- ﬁcation. The objective is to minimize the sum of the information acquisition cost and misclassiﬁcation cost. Any decision theoretic strategy tackling this problem needs to compute value of information for sets of features. Having calculated this information, different acquisition strategies are possible (acquiring one feature at time, acquiring features in sets, etc.). However, because the value of information calculation for arbitrary subsets of features is computationally intractable, most traditional approaches have been greedy, computing values of features one at a time. We make the problem of value of information calculation tractable in practice by introducing a novel data structure called the Value of Information Lattice (VOILA). VOILA exploits dependencies between missing features and makes sharing of information value computations between different feature subsets possible. To the best of our knowledge, performance differences between greedy acquisition, acquiring features in sets, and a mixed strategy have not been investigated empirically in the past, due to inherit intractability of the problem. With the help of VOILA, we are able to evaluate these strategies on ﬁve real world datasets under various cost assumptions. We show that VOILA reduces computation time dramatically. We also show that the mixed strategy outperforms both greedy acquisition and acquisition in sets.

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