Learning Boolean concepts in the presence of many irrelevant features

Almuallim, Hussein; Dietterich, Thomas G.

doi:10.1016/0004-3702(94)90084-1

Cited by 423 publications

(181 citation statements)

References 10 publications

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“…We chose three representative subset evaluation measures in combination with SF search engine. One, denoted by SF W , uses a target learning algorithm to estimate the worth of gene subsets; the other two are subset search algorithms which exploit sequential forward search and use correlation measures (variation of CFS Correlation-based Feature Selection algorithm (Hall, 2000)) or consistency measure (variation of FOCUS (Almuallim and Dietterich, 1994)) to guide the search, denoted by CF S SF and F OCUS SF , respectively (both of them used in (Yu and Liu, 2004a)). …”

Section: Resultsmentioning

confidence: 99%

Incremental wrapper-based gene selection from microarray data for cancer classification

Ruíz

Riquelme

Aguilar–Ruiz

2006

Pattern Recognition

251

100

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Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.

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Section: Resultsmentioning

confidence: 99%

Incremental wrapper-based gene selection from microarray data for cancer classification

Ruíz

Riquelme

Aguilar–Ruiz

2006

Pattern Recognition

251

100

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“…In this section we describe the use of relevance in FSS briefly. In the filter category we look at RELIEFF (Kira & Rendell, 1992), FOCUS (Almuallim & Dietterich, 1991;Almuallim & Dietterich, 1994) and Schlimmer's approach (Schlimmer, 1993), and in the wrapper category we look at the work by John, Kohavi, and Pfleger (1994).…”

Section: The Use Of Relevance In Fssmentioning

confidence: 99%

Untitled

2000

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Abstract. The notion of relevance is used in many technical fields. In the areas of machine learning and data mining, for example, relevance is frequently used as a measure in feature subset selection (FSS). In previous studies, the interpretation of relevance has varied and its connection to FSS has been loose. In this paper a rigorous mathematical formalism is proposed for relevance, which is quantitative and normalized. To apply the formalism in FSS, a characterization is proposed for FSS: preservation of learning information and minimization of joint entropy. Based on the characterization, a tight connection between relevance and FSS is established: maximizing the relevance of features to the decision attribute, and the relevance of the decision attribute to the features. This connection is then used to design an algorithm for FSS. The algorithm is linear in the number of instances and quadratic in the number of features. The algorithm is evaluated using 23 public datasets, resulting in an improvement in prediction accuracy on 16 datasets, and a loss in accuracy on only 1 dataset. This provides evidence that both the formalism and its connection to FSS are sound.

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“…The optimal selection can only be done by testing all possible sets of M features chosen from N, To deal with the problem of feature selection, many methods have been proposed. In general, they can be classified into two categories: (1) the filter approach, which serves as a filter to sieve the irrelevant and/or redundant features without taking into account the induction algorithm [1][5] [10]; and (2) the wrapper approach, which uses the induction algorithm itself as a black box in the phase of attributes selection to select a good features subset which improve the performance, i.e. the accuracy of the induction algorithm [4] [8][11] [16].…”

Section: Introductionmentioning

confidence: 99%

“…Almuallim and Dietterich MIG: In order to improve FOCUS algorithm, A1-muallim and Dietterich in [1] proposed tree heuristics for the MIN-FEATURES bias. The Mutual-Information-Greedy algorithm use the entropy measure to evaluate a subset entirely.…”

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confidence: 99%

A hybrid approach to feature selection

Boussouf

1998

Principles of Data Mining and Knowledge Discovery

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Abstract. The irrelevant and redundant features may degrade the learner speed (due to the high dimensionality) and reduce both the accuracy and comprehensibility of induced model. To cope with these problems, many methods have been proposed to select a subset of pertinent leatures. In order to evaluate these subsets, two main approaches are generally distinguished: (1) filter approach: which considers only data i.e. algorithm-independent; (2) wrapper approach: which takes into account both data and a given learning algorithm i.e. algorithm-dependent. In this paper, we address the problem of subset selection using a-RST (a generalized rough sets theory). We propose an algorithm to find a set of (~-reducts which are non deterministic reducts. To select the best one among them, we also propose a Hybrid Approach by putting filter and wrapper together to overcome the disadvantages of each approach. Our study shows that generally the highest-accuracy-subset is not the best one as regards to the filter criteria. The highest accuracy subset is found by the new approach with minimum cost.

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Learning Boolean concepts in the presence of many irrelevant features

Cited by 423 publications

References 10 publications

Incremental wrapper-based gene selection from microarray data for cancer classification

Incremental wrapper-based gene selection from microarray data for cancer classification

Untitled

A hybrid approach to feature selection

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