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The purpose of this paper is to investigate the impact of market and organizational determinants on the voluntary disclosure level of Egyptian companies.

Uses a disclosure index of voluntary disclosure that is based upon the following information categories: strategic information; financial information; non‐financial information; and future prospect information to rate the level of disclosure. Multivariate analysis, voluntary disclosure determinants: earnings quality; ownership structure; competition intensity; information asymmetry, and possible relationships with disclosure level provide the basis for discussion.

It is found that the level of voluntary disclosure in the emerging market of Egypt ranges from low to moderate level. There is no significant relationship between a company's voluntary disclosure level and earnings quality and competition intensity, while this relationship is significant for information asymmetry and ownership structure.

The results are constrained by the proxies that represent non‐financial factors of the market.

This paper extends prior studies on voluntary disclosure in Egypt by looking at a comprehensive set of market and organizational factors that might affect the disclosure level, based on a structured disclosure index of strategic, financial and non‐financial, and future prospect information. The findings would help boards of directors to explain the adoption of certain disclosure strategies, and understand the corporate disclosure behavior.

Ensemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems. Despite the effectiveness of these techniques, studies have shown that ensemble methods generate a large number of hypotheses and that contain redundant classifiers in most cases. Several works proposed in the state of the art attempt to reduce all hypotheses without affecting performance.

In this work, the authors are proposing a pruning method that takes into consideration the correlation between classifiers/classes and each classifier with the rest of the set. The authors have used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by a technique inspired by the CFS (correlation feature selection) algorithm.

The proposed method CES (correlation-based Ensemble Selection) was evaluated on ten datasets from the UCI machine learning repository, and the performances were compared to six ensemble pruning techniques. The results showed that our proposed pruning method selects a small ensemble in a smaller amount of time while improving classification rates compared to the state-of-the-art methods.

CES is a new ordering-based method that uses the CFS algorithm. CES selects, in a short time, a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-the-art techniques used in this study.