Multiple Two-class Problems Research Articles

Ensemble of Nested Dichotomies 기법을 이용한 스마트폰 가속도 센서 데이터 기반의 동작 인지

Ensemble of Nested Dichotomies 기법을 이용한 스마트폰 가속도 센서 데이터 기반의 동작 인지

Using a Novel AdaBoost Algorithm and Chous Pseudo Amino Acid Composition for Predicting Protein Subcellular Localization

For a protein, an important characteristic is its location or compartment in a cell. This is because a protein has to be located in its proper position in a cell to perform its biological functions. Therefore, predicting protein subcellular location is an important and challenging task in current molecular and cellular biology. In this paper, based on AdaBoost.ME algorithm and Chou's PseAAC (pseudo amino acid composition), a new computational method was developed to identify protein subcellular location. AdaBoost.ME is an improved version of AdaBoost algorithm that can directly extend the original AdaBoost algorithm to deal with multi-class cases without the need to reduce it to multiple two-class problems. In some previous studies the conventional amino acid composition was applied to represent protein samples. In order to take into account the sequence order effects, in this study we use Chou's PseAAC to represent protein samples. To demonstrate that AdaBoost.ME is a robust and efficient model in predicting protein subcellular locations, the same protein dataset used by Cedano et al. (Journal of Molecular Biology, 1997, 266: 594-600) is adopted in this paper. It can be seen from the computed results that the accuracy achieved by our method is better than those by the methods developed by the previous investigators.

Semi-supervised multi-class Adaboost by exploiting unlabeled data

Semi-supervised learning has attracted much attention in pattern recognition and machine learning. Most semi-supervised learning algorithms are proposed for binary classification, and then extended to multi-class cases by using approaches such as one-against-the-rest. In this work, we propose a semi-supervised learning method by using the multi-class boosting, which can directly classify the multi-class data and achieve high classification accuracy by exploiting the unlabeled data. There are two distinct features in our proposed semi-supervised learning approach: (1) handling multi-class cases directly without reducing them to multiple two-class problems, and (2) the classification accuracy of each base classifier requiring only at least 1/K or better than 1/K (K is the number of classes). Experimental results show that the proposed method is effective based on the testing of 21 UCI benchmark data sets.

ADAPTIVE FEATURE SPACES FOR LAND COVER CLASSIFICATION WITH LIMITED GROUND TRUTH DATA

Classification of land cover based on hyperspectral data is very challenging because typically tens of classes with uneven priors are involved, the inputs are high dimensional, and there is often scarcity of labeled data. Several researchers have observed that it is often preferable to decompose a multiclass problem into multiple two-class problems, solve each such subproblem using a suitable binary classifier, and then combine the outputs of this collection of classifiers in a suitable manner to obtain the answer to the original multiclass problem. This approach is taken by the popular error correcting output codes (ECOC) technique, as well by the binary hierarchical classifier (BHC). Classical techniques for dealing with small sample sizes include regularization of covariance matrices and feature reduction. In this paper we address the twin problems of small sample sizes and multiclass settings by proposing a feature reduction scheme that adaptively adjusts to the amount of labeled data available. This scheme can be used in conjunction with ECOC and the BHC, as well as other approaches such as round-robin classification that decompose a multiclass problem into a number of two (meta)-class problems. In particular, we develop the best-basis binary hierarchical classifier (BB-BHC) and best basis ECOC (BB-ECOC) families of models that are adapted to "small sample size" situations. Currently, there are few studies that compare the efficacy of different approaches to multiclass problems in general settings as well as in the specific context of small sample sizes. Our experiments on two sets of remote sensing data show that both BB-BHC and BB-ECOC methods are superior to their nonadaptive versions when faced with limited data, with the BB-BHC showing a slight edge in terms of classification accuracy as well as interpretability.

Evolving accurate and compact classification rules with gene expression programming

Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform a local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for discovering classification rules by using gene expression programming (GEP), a new technique of genetic programming (GP) with linear representation. The antecedent of discovered rules may involve many different combinations of attributes. To guide the search process, we suggest a fitness function considering both the rule consistency gain and completeness. A multiclass classification problem is formulated as multiple two-class problems by using the one-against-all learning method. The covering strategy is applied to learn multiple rules if applicable for each class. Compact rule sets are subsequently evolved using a two-phase pruning method based on the minimum description length (MDL) principle and the integration theory. Our approach is also noise tolerant and able to deal with both numeric and nominal attributes. Experiments with several benchmark data sets have shown up to 20% improvement in validation accuracy, compared with C4.5 algorithms. Furthermore, the proposed GEP approach is more efficient and tends to generate shorter solutions compared with canonical tree-based GP classifiers.

Multi-class AdaBoost

Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we propose a new algorithm that naturally extends the original AdaBoost algorithm to the multiclass case without reducing it to multiple two-class problems. Similar to AdaBoost in the twoclass case, this new algorithm combines weak classifiers and only requires the performance of each weak classifier be better than random guessing (rather than 1/2). We further provide a statistical justification for the new algorithm using a novel multi-class exponential loss function and forward stage-wise additive modeling. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive with the best currently available multi-class classification methods.