K-nearest neighbor rule (KNN) is the wellknown non-parametric technique in the statistical pattern classification, owing to its simplicity, intuitiveness and effectiveness. In this paper, we firstly review the related works in brief and detailedly analyze the sensitivity issue on the choice of the neighborhood size k, existed in the KNN rule. Motivated by the problem, a novel dual weighted voti...

Although publicly accessible databases containing speech documents. It requires a great deal of time and effort required to keep them up to date is often burdensome. In an effort to help identify speaker of speech if text is available, text-mining tools, from the machine learning discipline, it can be applied to help in this process also. Here, we describe and evaluate document classification a...

In this paper, a novel k -nearest neighbors (kNN) weighting strategy is proposed for handling the problem of class imbalance. When dealing with highly imbalanced data, a salient drawback of existing kNN algorithms is that the class with more frequent samples tends to dominate the neighborhood of a test instance in spite of distance measurements, which leads to suboptimal classification performa...

An efficient classification framework for mapping agricultural tillage practice using hyperspectral remote sensing imagery is proposed, which has the potential to be implemented practically to provide rapid, accurate, and objective surveying data for precision agricultural management and appraisal from large-scale remote sensing images. It includes a local region filter [i.e., Gaussian low-pass...

Indoor positioning has attracted commercial developers and researchers in the last few decades. Global system (GPS) cannot well localize indoor environment. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map positions of individuals environments. The received signal strength indicator (RSSI) fingerprinting plays a key role access po...

Determining whether a site has a search interface is a crucial priority for further research of deep web databases. This study first reviews the current approaches employed in search interface identification for deep web databases. Then, a novel identification scheme using hybrid features and a feature-weighted instance-based learner is put forward. Experiment results show that the proposed sch...

Fault detection and diagnosis (FDD) systems can reduce high costs energy consumption. This paper presents a machine learning-based fault technique for actuators sensors in pressurized water reactor (PWR). In the proposed FDD framework, faults are first detected using shallow neural network. Second, is performed 15 different classifiers provided MATLAB Classification Learner toolbox, including s...

An intrusion detection system (IDS) detects unauthorized manipulations of computer systems. Operation as feature reduction (including feature extraction and feature selection) plays an important role in the sense of improving classification performance and reducing the computational complexity of intrusion detection system. Feature reduction is even more important at online detection when less ...

Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours ðKNNÞ algorithm. In this article, we propose a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI). This method provides a missing data estimation aimed at solving the c...