One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies

Zhou, Ligang; Wang, Qingyang; Fujita, Hamido

doi:10.1016/j.inffus.2016.11.009

Cited by 63 publications

(23 citation statements)

References 29 publications

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“…Apart from the traditional fusion, an approach to inject the knowledge to derive the actual information was proposed in study [99] ; it was developed as a defusing technique to infer the relevant base data from the aggregated historical data. Another application of fusion was given as a one-vs-one scheme with optimizing decision directed acyclic graph (ODDAG) to predict the listing status of companies [100] . One of the recent applications included the uncertain possibility–probability information fusion for stock selection based on the relative closeness of the alternative stocks [101] .…”

Section: Discussionmentioning

confidence: 99%

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

2021

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Investment in a financial market is aimed at getting higher benefits; this complex market is influenced by a large number of events wherein the prediction of future market dynamics is challenging. The investors’ etiquettes towards stock market may demand the need of studying various associated factors and extract the useful information for reliable forecasting. Fusion can be considered as an approach to integrate data or characteristics, in general, and enhance the prediction based on the combinational approach that can aid each other. We conduct a systematic approach to present a survey for the years 2011–2020 by considering articles that have used fusion techniques for various stock market applications and broadly categorize them into information fusion, feature fusion, and model fusion. The major applications of stock market include stock price and trend prediction, risk analysis and return forecasting, index prediction, as well as portfolio management. We also provide an infographic overview of fusion in stock market prediction and extend our survey for other finely addressed financial prediction problems. Based on our surveyed articles, we provide potential future directions and concluding remarks on the significance of applying fusion in stock market.

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Section: Discussionmentioning

confidence: 99%

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

2021

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“…An effective coding matrix would be beneficial for the ECOC algorithm. Common coding strategies fall into two categories [22]: (1) Data-independent category, including One-VS-All (OVA), One-VS-One (OVO) [23], and ordinal ECOC [18], where the coding matrices are predefined based on a simple partition of the label space, and without considering the relationship between the data features and data distribution; (2) Data-dependent category, such as D-ECOC [24] and ECOC-ONE [25], where the coding matrixes are generated based on the data distribution; ECOC-MDC [19] uses data complexity [26] to guide the generation of the coding matrix. In contrast, the data-dependent ECOC algorithms use the inherent information of the data to guide the generation of the coding matrices, which are intuitively better suited to handle difficult multi-classification tasks.…”

Section: Background a Overview Of Error-correcting Output Codes mentioning

confidence: 99%

Imprecise Deep Forest for Partial Label Learning

et al. 2020

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“…However, the Micro averaging does not provide an accurate measure of performance when the instances are not equally distributed over the classes (most instances belong to one class). Unlike Micro averaging, Macro averaging provides meaningful performance measure despite that data is not equally representative of all classes (imbalanced classes) [58]. Therefore, Macro averaging is used as a measure to evaluate the multi-class model performance in this study.…”

Section: Performance Measuresmentioning

confidence: 99%

Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context

Rachid¹,

Amine²,

Bouikhalene³

et al. 2018

IJECE

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<p><span>With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors’ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-Recency-Frequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macro-averaging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers’ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.</span></p>

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One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies

Cited by 63 publications

References 29 publications

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

Imprecise Deep Forest for Partial Label Learning

Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context

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