Instance is important in data analysis and mining; it filters out unrepresentative, redundant, or noisy data from a given training set to obtain effective model learning. Various instance selection algorithms are proposed in the literature, and their potential and applicability in data cleaning and preprocessing steps are demonstrated. For multiclass classification datasets, the existing instance selection algorithms must deal with all the instances across the different classes simultaneously to produce a reduced training set.Generally, every multiclass classification dataset can be regarded as a complex domain problem, which can be effectively solved using the divide-and-conquer principle. In this study, the one-versus-all (OVA) and one-versus-one (OVO) decomposition approaches were used to decompose a multiclass dataset into multiple binary class datasets. These approaches have been widely employed when constructing the classifier but have never been considered in instance selection. The results of instance selection performance obtained with the OVA, OVO, and baseline approaches were assessed and compared for 20 different domain multiclass datasets as the first study and five medical domain datasets as the validation study. Furthermore, three instance selection algorithms were compared, including IB3, DROP3, and GA. The results demonstrate that using the OVO approach to perform instance selection can make the support vector machine (SVM) and k-nearest neighbour (k-NN) classifiers perform significantly better than the OVA and baseline approaches in terms of the area under the ROC curve (AUC) rate, regardless of the instance selection algorithm used. Moreover, the OVO approach can provide reasonably good data reduction rates and processing times, which are all better than those of the OVA approach.

This study investigates the existence of quarterly accrual anomalies in Taiwan's stock market. Our empirical results indicate that the arbitrage portfolios formed by the accounting accruals of the fourth‐quarter financial reports could result in abnormal returns, and thus, inferred that quarterly accrual anomalies exist. We further examined the causes of the accrual anomalies and determined that the manager conduct earnings management behavior during the fourth quarter. Managers execute discretionary accruals for management earnings forecast to meet previously issued financial forecasts, which leads to the fourth‐quarter accrual anomalies. The portfolios formed by using discretionary accruals and management earnings forecasts produce arbitrage portfolios with optimal return.