Classification tasks are usually analysed and improved through new model architectures or hyperparameter optimisation but the underlying properties of datasets are discovered on an ad-hoc basis as errors occur. However, understanding the properties of the data is crucial in perfecting models. In this paper we analyse exactly which characteristics of a dataset best determine how difficult that dataset is for the task of text classification. We then propose an intuitive measure of difficulty for text classification datasets which is simple and fast to calculate. We show that this measure generalises to unseen data by comparing it to state-of-the-art datasets and results. This measure can be used to analyse the precise source of errors in a dataset and allows fast estimation of how difficult a dataset is to learn. We searched for this measure by training 12 classical and neural network based models on 78 real-world datasets, then use a genetic algorithm to discover the best measure of difficulty. Our difficulty-calculating code ( this https URL ) and datasets ( this http URL ) are publicly available.
One source of difficulty in a dataset is mislabelled items of data (noise). showed that filtering noise could produce large gains in model performance, potentially yielding larger improvements than hyperparameter optimisation @cite_0 . We ignored noise in this work because it can be reduced with proper data cleaning and is not a part of the true signal of the dataset. We identified four other areas of potential difficulty which we attempt to measure:
@cite_0
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Data mining and knowledge discovery aim at producing useful and reliable models from the data. Unfortunately some databases contain noisy data which perturb the generalization of the models. An important source of noise consists of mislabelled training instances. We offer a new approach which deals with improving classification accuracies by using a preliminary filtering procedure. An example is suspect when in its neighbourhood defined by a geometrical graph the proportion of examples of the same class is not significantly greater than in the database itself. Such suspect examples in the training data can be removed or relabelled. The filtered training set is then provided as input to learning algorithms. Our experiments on ten benchmarks of UCI Machine Learning Repository using 1-NN as the final algorithm show that removal gives better results than relabelling. Removing allows maintaining the generalization error rate when we introduce from 0 to 20 of noise on the class, especially when classes are well separable. The filtering method proposed is finally compared to the relaxation relabelling schema. Removing objects that are noisy is an important goal of data cleaning as noise hinders most types of data analysis. Most existing data cleaning methods focus on removing noise that is the product of low-level data errors that result from an imperfect data collection process, but data objects that are irrelevant or only weakly relevant can also significantly hinder data analysis. Thus, if the goal is to enhance the data analysis as much as possible, these objects should also be considered as noise, at least with respect to the underlying analysis. Consequently, there is a need for data cleaning techniques that remove both types of noise. Because data sets can contain large amounts of noise, these techniques also need to be able to discard a potentially large fraction of the data. This paper explores four techniques intended for noise removal to enhance data analysis in the presence of high noise levels. Three of these methods are based on traditional outlier detection techniques: distance-based, clustering-based, and an approach based on the local outlier factor (LOF) of an object. The other technique, which is a new method that we are proposing, is a hyperclique-based data cleaner (HCleaner). These techniques are evaluated in terms of their impact on the subsequent data analysis, specifically, clustering and association analysis. Our experimental results show that all of these methods can provide better clustering performance and higher quality association patterns as the amount of noise being removed increases, although HCleaner generally leads to better clustering performance and higher quality associations than the other three methods for binary data. Data mining techniques are commonly used to construct models for identifying software modules that are most likely to contain faults. In doing so, an organization's limited resources can be intelligently allocated with the goal of detecting and correcting the greatest number of faults. However, there are two characteristics of software quality datasets that can negatively impact the effectiveness of these models: class imbalance and class noise. Software quality datasets are, by their nature, imbalanced. That is, most of a software system's faults can be found in a small percentage of software modules. Therefore, the number of fault-prone, fp, examples (program modules) in a software project dataset is much smaller than the number of not fault-prone, nfp, examples. Data sampling techniques attempt to alleviate the problem of class imbalance by altering a training dataset's distribution. A program module contains class noise if it is incorrectly labeled. While several studies have been performed to evaluate data sampling methods, the impact of class noise on these techniques has not been adequately addressed. This work presents a systematic set of experiments designed to investigate the impact of both class noise and class imbalance on classification models constructed to identify fault-prone program modules. We analyze the impact of class noise and class imbalance on 11 different learning algorithms (learners) as well as 7 different data sampling techniques. We identify which learners and which data sampling techniques are most robust when confronted with noisy and imbalanced data. We analyse the noise arising in collaborative filtering when formalised as a probabilistic matrix factorisation problem. We show empirically that modelling row- and column-specific variances is important, the noise being in general non-Gaussian and heteroscedastic. We also advocate for the use of a Student-t prior for the latent features as the standard Gaussian is included as a special case. We derive several variational inference algorithms and estimate the hyperparameters by type-II maximum likelihood. Experiments on real data show that the predictive performance is significantly improved.