Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

Güvenir, H. Altay; Demiroz, Gulsen; İlter, Nilsel

doi:10.1016/s0933-3657(98)00028-1

Cited by 206 publications

(101 citation statements)

References 12 publications

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“…They project the training instances on each feature separately, and then generalize on these projections to form intervals [6,[13][14][15][16][17][18]45]. In those studies, segments (intervals) are taken to be the basic unit of concept representation; and the classification knowledge is represented in the form of segments formed on each feature.…”

Section: Feature Projections Conceptmentioning

confidence: 99%

“…Ko and Seo applied feature projections to the text categorization problem [27]. In the work presented in [17,18,45], a segment represents examples from a single class, whereas the authors in [6,14,15] allow a segment to represent examples from a set of classes instead of a single class. We prefer to define the segment term to be a unit of concept description that represents examples from a set of classes.…”

Section: Feature Projections Conceptmentioning

confidence: 99%

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Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Aydın

Güvenir

2009

Knowledge-Based Systems

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a b s t r a c tIn a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as ''association rule set stream" or ''streaming association rules" and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones.

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Section: Feature Projections Conceptmentioning

confidence: 99%

Section: Feature Projections Conceptmentioning

confidence: 99%

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Aydın

Güvenir

2009

Knowledge-Based Systems

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“…Several works have been carried out in the literature [3,5,6] in order to define classifier systems able to solve this problem. In this work we present a novel neuro-genetic approach, that uses evolutionary algorithms to optimize a particular kind of neural networks, that would work as multi-classifier systems in this dermatological application.…”

Section: Dermatology Classification Problemmentioning

confidence: 99%

Dermatology Disease Classification via Novel Evolutionary Artificial Neural Network

Tzitzikas

Analyti

2007

18th International Conference on Database and Expert Systems Applications (DEXA 2007)

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Neuro-genetic systems are biologically inspired computational models that use evolutionary algorithms (EAs) in conjunction with neural networks (NNs) to solve problems. They are especially useful in classification problems in which classifier systems are not able to provide easy answers. In this paper a novel neuro-genetic approach is used in order to predict a known classification problem, related to dermatology diseases.

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“…The VFC previously developed, e.g., CFP (Güvenir & Sirin, 1996), VFI (Güvenir, Demiröz, & Ilter, 1998), BCFP (Güvenir, Emeksiz, Ikizler, & Örmeci, 2004), learn a set of rules that contain a single condition based on a single feature in their antecedent. Given a query, each feature, based on the value of the query instance for that feature, distributes its vote among possible classes.…”

Section: Introductionmentioning

confidence: 99%

Voting features based classifier with feature construction and its application to predicting financial distress

Güvenir

Çakır

2010

Expert Systems with Applications

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a b s t r a c tVoting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms.

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Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

Cited by 206 publications

References 12 publications

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Dermatology Disease Classification via Novel Evolutionary Artificial Neural Network

Voting features based classifier with feature construction and its application to predicting financial distress

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