G3P-MI: A genetic programming algorithm for multiple instance learning

Zafra, Amelia; Ventura, Sebastián

doi:10.1016/j.ins.2010.07.031

Cited by 27 publications

(12 citation statements)

References 56 publications

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“…However, to the best of our knowledge there are no GPU-based implementations of multi-instance classification rules algorithms to date. G3P-MI [48] is a Grammar-Guided Genetic Programming (G3P) [26] algorithm for multi-instance learning. It is based on the presence-based hypothesis and has demonstrated accurate classification and better performance than many other multi-instance methods.…”

Section: Rule-based Modelsmentioning

confidence: 99%

“…It computes the confusion matrix values to calculate some well-known indicators in classification such as sensitivity, specificity, precision, recall, F-Measure, etc. For instance, the goal of the G3P-MI [48] algorithm is to maximize both sensitivity and specificity, computing fitness as their product.…”

Section: Fitness Kernelmentioning

confidence: 99%

“…Similarly, there are many machine learning methods available to solve these problems, such as multiinstance lazy learning algorithms [43], multi-instance tree learners [8], multi-instance rule inducers [9], multi-instance bayesian approaches [27], multi-instance kernel methods [22,38], multi-instance ensembles [45,51], and evolutionary algorithms [48,50]. However, most of the MIL algorithms are very slow and cannot be applied to large data sets.…”

Section: Introductionmentioning

confidence: 99%

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Speeding up multiple instance learning classification rules on GPUs

2014

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Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second.

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Section: Rule-based Modelsmentioning

confidence: 99%

Section: Fitness Kernelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speeding up multiple instance learning classification rules on GPUs

2014

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“…The use of ensembles also has been considered in this learning, the works of Xu and Frank (2004), Zhang and Zhou (2005), and Zhou and Zhang (2007) are examples of this paradigm. Finally, we can find multi-instance evolutionary algorithms which adapt grammar-guided genetic programming to this scenario (Zafra 2010), but from a mono-objective perspective.…”

Section: Learning Techniques For Multiple Instance Learningmentioning

confidence: 99%

“…• Methods based on evolutionary algorithms Such a method employs grammar guided genetic programming adapted to MIL. The algorithm considered is G3P-MI (Zafra 2010) which could be considered a monoobjective version of this proposal. Table 6 shows the average results of accuracy, sensitivity and specificity for all algorithms in each data set.…”

Section: Comparison With Other Proposalsmentioning

confidence: 99%

Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems

Zafra

Ventura

2011

Soft Comput

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Multiple instance learning (MIL) is considered a generalization of traditional supervised learning which deals with uncertainty in the information. Together with the fact that, as in any other learning framework, the classifier performance evaluation maintains a trade-off relationship between different conflicting objectives, this makes the classification task less straightforward. This paper introduces a multi-objective proposal that works in a MIL scenario to obtain well-distributed Pareto solutions to multi-instance problems. The algorithm developed, MultiObjective Grammar Guided Genetic Programming for Multiple Instances (MOG3P-MI), is based on grammarguided genetic programming, which is a robust tool for classification. Thus, this proposal combines the advantages of the grammar-guided genetic programming with benefits provided by multi-objective approaches. First, a study of multi-objective optimization for MIL is carried out. To do this, three different extensions of MOG3P-MI are designed and implemented and their performance is compared. This study allows us on the one hand, to check the performance of multi-objective techniques in this learning paradigm and on the other hand, to determine the most appropriate evolutionary process for MOG3P-MI. Then, MOG3P-MI is compared with some of the most significant proposals developed throughout the years in MIL. Computational experiments show that MOG3P-MI often obtains consistently better results than the other algorithms, achieving the most accurate models. Moreover, the classifiers obtained are very comprehensible.

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Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

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Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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G3P-MI: A genetic programming algorithm for multiple instance learning

Cited by 27 publications

References 56 publications

Speeding up multiple instance learning classification rules on GPUs

Speeding up multiple instance learning classification rules on GPUs

Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems

Chemical complexity challenge: Is multi‐instance machine learning a solution?

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