A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity

Bouaguel, Waad; Mufti, Ghazi Bel; Limam, Mohamed

doi:10.1201/b18231-21

Cited by 5 publications

(5 citation statements)

References 1 publication

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“…Decision Trees (DTs), or Decision Tree Classifiers (DTCs), are a supervised machine learning method and are one of many classifiers from statistical-based algorithms [42] which have been successfully applied in a variety of fields for digital image classification [43]. A DTC is a non-parametric classifier [44], which is an effective and popular technique for classification [45].…”

Section: Decision Tree Classificationmentioning

confidence: 99%

An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data

Hamad¹

2020

AEES

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Remotely sensed images serve as a valuable source of present and archival information since they provide the geographical distribution of natural and cultural features both spatially and temporally, as well as objects on the earth's surface. Three machine learning classifiers, namely artificial neural networks (ANN), support vector machines (SVM), and decision tree (DT) algorithms, were applied in order to classify the Sentinel-2A data over the city of Soran. The differences in classification accuracies were evaluated by the confusion matrix. The supervised ANNs obtained the most accurate classification accuracy as compared with support SVM and DTs algorithms. Furthermore, the overall accuracy assessment of ANN was 90%, with SVM at 65%, while DTs were 60%. It can be concluded that ANNs can provide the best classification machine learning technique for land cover classification.

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Section: Decision Tree Classificationmentioning

confidence: 99%

An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data

Hamad¹

2020

AEES

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“…Searching for the optimal value of k, i.e., the one which minimizes the error, we found that for the Full Information method, the smallest error is being received for k = 5 whereas for the Random Elimination and Options Menu methods it is being received for k = 10. In order to overcome the risk for overfitting we used post-pruning (Osei-Bryson 2007). Additionally, for every learning experiment, we made sure that the users participated in the training set cases are not the same as the ones participated in the test set cases.…”

Section: Using Dynamic Pricingmentioning

confidence: 99%

Making Money from What You Know - How to Sell Information?

Alkoby

Wang

Sarne

et al. 2019

AAAI

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Information plays a key role in many decision situations. The rapid advancement in communication technologies makes information providers more accessible, and various information providing platforms can be found nowadays, most of which are strategic in the sense that their goal is to maximize the providers’ expected profit. In this paper, we consider the common problem of a strategic information provider offering prospective buyers information which can disambiguate uncertainties the buyers have, which can be valuable for their decision making. Unlike prior work, we do not limit the information provider’s strategy to price setting but rather enable her flexibility over the way information is sold, specifically enabling querying about specific outcomes and the elimination of a subset of non-true world states alongside the traditional approach of disclosing the true world state. We prove that for the case where the buyer is self-interested (and the information provider does not know the true world state beforehand) all three methods (i.e., disclosing the true worldstate value, offering to check a specific value, and eliminating a random value) are equivalent, yielding the same expected profit to the information provider. For the case where buyers are human subjects, using an extensive set of experiments we show that the methods result in substantially different outcomes. Furthermore, using standard machine learning techniques the information provider can rather accurately predict the performance of the different methods for new problem settings, hence substantially increase profit.

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“…These extracted rules are usually used for the purpose of classification/prediction. Compared to other text mining techniques, it is widely applied in various areas since it is robust to data scales or distributions [5].…”

Section: Decision Tree Algorithmsmentioning

confidence: 99%

A Comparative Study on Serial Decision Tree Classification Algorithms in Text Mining

Almunirawi¹,

Maghari²

2016

IJICR

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Text mining refers to the process of deriving high quality information from text. It is used in search engine, digital libraries, fraud detection, and other applications that handles text data. Text mining tasks include text classification, text clustering, entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling. Classification of objects into pre-defined categories based on their features is a widely studied problem. It aims to employ labeled training data set to build a classification model based on other attributes, such that the model can be used to classify new data according to their class labels. The decision tree-based classification is one of the most practical and effective methods that uses inductive learning. It is implemented serially or in parallel, depending on data set size. Some of the classifiers such as SLIQ, SPRINT and Rainforest can be implemented serially or parallel. ID 3, CART and C4.5 are serial classifiers. In this paper, we review various decision tree algorithms with their limitations, and conduct a comparative study to evaluate their performance regarding accuracy, learning time and tree size, using four sample datasets. We found out that Random Forest classifier is the most accurate one among other classifiers. However, the increase of the dataset size and its attributes, the more the learning time and tree size, and vise versa.

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A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity

Cited by 5 publications

References 1 publication

An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data

An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data

Making Money from What You Know - How to Sell Information?

A Comparative Study on Serial Decision Tree Classification Algorithms in Text Mining

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