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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
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Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data

机译：与决策树相结合的特征选择和微阵列数据的学习

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摘要

One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of the well-known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with a much smaller number of genes while giving competitive classification accuracy but also having tight risk guarantees on future performance, unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.
机译：设计特征选择学习算法的目标之一是获得依赖于少量属性并具有可验证的未来性能保证的分类器。很少有方法可以成功地同时解决这两个目标。据我们所知，迄今为止尚未在基因表达数据的分类中提出这种对未来性能给出理论界限的算法。在这项工作中，我们研究了在Occam的Razor，样本压缩和PAC-Bayes学习设置中学习决策树桩的连合（或析取）的前提，以识别可用于执行可靠分类任务的一小部分属性。我们将提出的方法用于从DNA微阵列数据中鉴定基因，并将我们的结果与为该任务提出的众所周知的成功方法进行比较。我们表明，与其他方法不同，我们的算法不仅能够找到具有较少基因数量的假设，同时具有竞争性的分类准确性，而且对未来的表现具有严格的风险保证。在设计新颖算法和将其应用于其他领域方面，所提出的方法是通用且可扩展的。

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