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One of the challenges of data mining is finding hyperparameters for a learning algorithm that will produce the best model for a given dataset. Hyperparameter optimization automates this process, but it can still take significant time. It has been found that hyperparameter optimization does not always result in induced models with significant improvement over default values, yet no systematic analysis...
We present an application of a Multiple Instance Learning (MIL) approach to image classification. In particular we focus on a recent MIL method for binary classification where the objective is to discriminate between positive and negative sets of points. Such sets are called bags and the points inside the bags are called instances. In the case of two classes of instances (positive and negative), a...
We consider the semi-supervised dimension reduction problem: given a high dimensional dataset with a small number of labeled data and huge number of unlabeled data, the goal is to find the low-dimensional embedding that yields good classification results. Most of the previous algorithms for this task are linkage-based algorithms. They try to enforce the must-link and cannot-link constraints in dimension...
In the past few years, wireless sensor networks (WSNs) have been increasingly gaining impact in the real world with with various applications such as healthcare, condition monitoring, control networks, etc. Anomaly detection in WSNs is an important aspect of data analysis in order to identify data items which does not conform to an expected pattern or other items in a dataset. This paper describes...
The rapid development of high-throughput sequencing technology provides unique opportunities for studies of transcription factor binding, while also bringing new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computational cost, most of them have...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front...
Support Vector Machines (SVMs) are supervised learning models of the machine learning field whose performance strongly depended on its hyperparameters. The Bio-inspired Optimization Tool for SVM (BIOTS) tool is based on a Multi-Objective Particle Swarm Algorithm (MOPSO) to tune hyperparameters of SVMs. In this work, BIOTS is proposed along with a custom hardware design generator (VHDL) that implements...
The goal of complex event detection is to automatically detect whether an event of interest happens in temporally untrimmed long videos which usually consist of multiple video shots. Observing some video shots in positive (resp. negative) videos are irrelevant (resp. relevant) to the given event class, we formulate this task as a multi-instance learning (MIL) problem by taking each video as a bag...
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
One-class support vector machines (OCSVM) have been recently applied to detect anomalies in wireless sensor networks (WSNs). Typically, OCSVM is kernelized by radial bais functions (RBF, or Gausian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of anomalies, which is rarely applicable in practice. This article investigates the application of OCSVM to detect anomalies...
In view of the support vector machine (SVM) model applied in vibrant fault diagnosis for hydro-turbine generating unit, it exists problems of parameter settings and classification-plane incline due to unequal sample, which leads to lower diagnosis accuracy. As a new bionic intelligent optimization algorithm for glowworm swarm optimization(GSO), it has the characteristics of strong versatility and...
As the emerging development of IoT circumstance, on-line detections or observations of a system states become easier by facilitating its corresponding multi-sensory responses, and thus the description of a system behavior becomes clearer. Abundant on-line multi-channel information from the embedded sensors would be advantageous to the understanding of the system. Though having the information, it...
In this paper, the operating conditions of vehicle internal combustion engine (ICE) waste heat utilization system are monitored by improved support vector machines (SVMs). Organic Rankine Cycle (ORC) is used to recover the ICE waste heat. Several optimal approaches are employed when training SVMs. The improved SVMs are then employed to monitor the operating conditions of the ICE waste heat recovery...
This paper is the first to address the problem of unsupervised action localization in videos. Given unlabeled data without bounding box annotations, we propose a novel approach that: 1) Discovers action class labels and 2) Spatio-temporally localizes actions in videos. It begins by computing local video features to apply spectral clustering on a set of unlabeled training videos. For each cluster of...
In water flooding oilfield, petroleum production is the most crucial target for production-injection wells system. An effective, informative and accurate production prediction facilitates parameter adjustment, production optimization, fault analysis and decrease in production cost. Some effective Artificial Intelligence (AI) technologies have been widely used in various kinds of industrial fields...
Finding efficient solutions for search and optimisation problems has inspired many researchers to utilise nature informed algorithms, where the interactions in swarm could lead to promising solutions for challenging problems. One problem in machine learning is class imbalance, which occurs in real-world applications such as medical diagnosis. This problem can bias the classification or make it entirely...
We have developed a fast distribution control method based on formula manipulation for low-resource environment like PLC (Programmable Logic Controller). The developed method consists of offline analysis and online optimization. In order to clarify the required specifications of implementation of the method, we analyzed the relationship between offline analysis result and online calculation time....
A novel projection twin support vector machine (PTSVM), termed as NPTSVM, is presented in this paper for binary classification. Although this method determines two projection vectors using the same way as PTSVM, it has more advantages than existing PTSVMs. First, NPTSVM does not have to calculate inverse matrices during the learning process, which makes the training speed of NPTSVM be much faster...
Differential evolution (DE) algorithm mainly uses the distance and direction information from the current population to guide search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. It can be used to extract the global information about...
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