with the aim of reducing cost of electricity consumption and peak load reduction, tools requirement for better managing electricity consumption have become inevitable in recent years. smart home has some equipment which are controllable and this ability is used for increasing comfort and minimizing electricity cost for residence. as a key component of smart home , electric vehicle(ev) ,increase...

With the aim of reducing cost of electricity consumption and peak load reduction, tools requirement for better managing electricity consumption have become inevitable in recent years. Smart home has some equipment which are controllable and this ability is used for increasing comfort and minimizing electricity cost for residence. As a key component of smart home , Electric Vehicle(EV) ,increase...

Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into fe...

Multiclass classification is an important and fundamental problem in machine learning. A popular family of multiclass classification methods belongs to reducing multiclass to binary based on output coding. Several multiclass boosting algorithms have been proposed to learn the coding matrix and the associated binary classifiers in a problemdependent way. These algorithms can be unified under a s...

We propose a novel multiclass classification algorithm Gentle Adaptive Multiclass Boosting Learning (GAMBLE). The algorithm naturally extends the two class Gentle AdaBoost algorithm to multiclass classification by using the multiclass exponential loss and the multiclass response encoding scheme. Unlike other multiclass algorithms which reduce the K-class classification task to K binary classifi...

We consider loss functions for multiclass prediction problems. We show when a multiclass loss can be expressed as a “proper composite loss”, which is the composition of a proper loss and a link function. We extend existing results for binary losses to multiclass losses. We subsume results on “classification calibration” by relating it to properness. We determine the stationarity condition, Breg...

We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping ...

Selection of relevant genes that will give higher accuracy for sample classification (for example, to distinguish cancerous from normal tissues) is a common task in most microarray data studies. An evolutionary method based on generalization error bound theory of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently. The bound theo...

Cost-sensitive multiclass classification has recently acquired significance in several applications, through the introduction of multiclass datasets with well-defined misclassification costs. The design of classification algorithms for this setting is considered. It is argued that the unreliable performance of current algorithms is due to the inability of the underlying loss functions to enforc...