Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly. However, experiments have revealed that multi-label heads are unlikely to be learned by existing methods due to their restrictiveness. To overcome this limitation, we propose a plug-in approach that relaxes the search space pruning used by existing methods in order to introduce a bias towards larger multi-label heads resulting in more expressive rules. We further demonstrate the effectiveness of our approach empirically and show that it does not come with drawbacks in terms of training time or predictive performance.Comment: Preprint version. To appear in Proceedings of the 22nd International Conference on Discovery Science, 201

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Enhancement of Optical and Thermal Properties of γ-Glycine Single Crystal: in the Presence of 2-Aminopyridine Potassium Chloride

International audienceIn this research paper, an overview of polymorph γ-form glycine single crystal crystallization in the presence of 2-aminopyridine potassium chloride as an additive at an ambient temperature by slow evaporation solution growth technique (SEST) has been presented. FTIR and NMR studies confirm the presence of functional groups in the grown crystal. In the UV–Visible NIR optical absorption spectral studies from 200 nm to 900 nm, the observed 0% absorption with lower cutoff wave length at 240 nm and high band gap (5. 5eV) enabled enhanced linear optical properties. Powder XRD study confirms crystalline nature of the grown γ-glycine crystal. The single crystal XRD study shows that the grown crystal possesses hexagonal structure and belongs to space group P31 with the cell parameters a=7. 09 Å; b=7. 09; c=5. 52 Å; α = β = 90˚; and γ = 120˚. Thermal studies have been carried out to identify the elevated thermal stability and decomposition temperature of the grown sample. Dielectric studies of as grown γ-glycine crystal exhibit low dielectric constant at higher frequencies, which is most essential parameters for nonlinear optical applications. Enhanced SHG efficiency of the grown crystal was confirmed by the Kurtz powder technique using Nd:YAG laser and found 1. 6 times greater than that of inorganic standard potassium dihydrogen phosphate. 1. Introduction. Highly polarizable conjugated system of organic molecule possesses non-centro symmetry structure and the inorganic molecule (anion), linking through hydrogen bond with organic molecule (cation) yields strong mechanical and high thermal stability [1, 2]. Molecular charge transfer induced in semiorganic complex by delocalized π electron, such that moving between electron donor and electron acceptor which are in opposite sides of the molecules [3, 4]. In the base acid interaction of organic and inorganic molecules, there is a high polarizable cation derived from aromatic nitro systems, linked to the polarizable anion of inorganic molecules through hydrogen bond network yields a noncentrosymmetric structural systems and this hydrogen bonding energy between organic and inorganic molecules made the dipole moment in parallel fashion ensures the increase of second harmonic generation activity [5]. The structures of 2-aminopyridine complexes have already been studied by Chao and his co-workers [6]. In recent years metal organic complexes have been played reasonable attention in advancement of technology [2,7]. Growth of 2-aminopyridine complex crystals is widely used in the rapid advancement in technology, such as ultra-fast phenomena, optical communication and optical storage devices , frequency doublers and optical modulators [8]. Optical properties of 2-aminopyridine complexes and their suitability for optoelectronic devices have been reported [9-14]. Metal organic nonlinear optical crystals possess good second harmonic generation efficiency, hence rich demand in optical storage devices, color display units and optical communication systems [7]. Recent research focus is on designing of new materials capable of attaining SHG processes by strong interaction with an oscillating field of light. Amino acids with ionic salt complex crystals have been investigated and recognized as materials having good nonlinear optical properties [1,3,15-17]. In this present work, synthesis and crystallization of glycine into γ-form glycine in the presence of aqueous solution 2-aminopyridine potassium chloride and their suitability for device fabrication with various enhanced optical and thermal properties are reported

Real Time Soil Moisture (RTSM) Based Irrigation Scheduling to Improve Yield and Water-Use Efficiency of Green Pea (<i>Pisum sativum</i> L.) Grown in North India

A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main plots consisted of fourirrigation levels at different matric potential ranges (I1: −20 kPa; I2: −30 kPa; I3: −35 kPa; and I4: −40 kPa), while the sub-plots consisted of three fertigation levels (F1: 120%, F2: 100% and F3: 80%) of recommended dose of fertilizers (40:60:50 kg/ha of NPK). The tensiometer with digital pressure transducer transferred the soil matric potential data to the irrigation controller, which activated the solenoid valves for irrigation. Observations were collected on plant growth parameters, pod yield, and quality parameters. Descriptive statistics of different plant growth parameters were made. The higher SMP threshold (−20 kPa) and lower SMP threshold (−40 kPa) greatly reduced the yield and water-use efficiency. Considering the results, real-time soil moisture-based irrigation at the soil matric potential threshold level of −30 kPa with 120% of recommended dose of fertilizers through fertigation was recommended for attaining maximum green pea pod yield and water-use efficiency under semi-arid Inceptisols