A study on mutual information-based feature selection for text categorization

Feature selection plays an important role in text categorization. Automatic feature selection methods such as document frequency thresholding (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization. Many existing experiments show IG is one of the most effective methods, by contrast, MI has been demonstrated to have relatively poor performance. According to one existing MI method, the mutual information of a category c and a term t can be negative, which is in conflict with the definition of MI derived from information theory where it is always non-negative. We show that the form of MI used in TC is not derived correctly from information theory. There are two different MI based feature selection criteria which are referred to as MI in the TC literature. Actually, one of them should correctly be termed "pointwise mutual information" (PMI). In this paper, we clarify the terminological confusion surrounding the notion of "mutual information" in TC, and detail an MI method derived correctly from information theory. Experiments with the Reuters-21578 collection and OHSUMED collection show that the corrected MI method’s performance is similar to that of IG, and it is considerably better than PMI

Impact of Biochar on the Bioremediation and Phytoremediation of Heavy Metal(loid)s in Soil

Anthropogenic activities, such as mining/smelting, result in the release and accumulation of heavy metal(loid)s in soil, posing serious human health and ecological risks. Due to the persistence of metal(loid)s, not undergoing any chemical and biological degradation, they can only be either immobilized or removed by, bioremediation and phytoremediation. Biochar is increasingly being recognized as a promising, effective material that can be used to remediate various contaminations including excessive heavy metals in soil. This chapter provides an overview of the state of the art on biochar resources, production processes and result of pyrolysis, surface characteristics of biochar, interactions of biochar with soil, and associated biota (microbes and plant). Furthermore, the understanding of characteristics of biochar and the interactions of biochar with soil and biota is necessary to assess the impacts of biochar on bioremediation and phytoremediation of heavy metal contaminated soil

Robust MIMO Detection With Imperfect CSI: A Neural Network Solution

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCO