Ordinal Decision Trees

In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.

Apple grading method based on neural network with ordered partitions and evidential ensemble learning

In order to improve the performance of the automatic apple grading and sorting system,in this paper,an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed.As a non-destructive grading method,apples are graded into three grades based on the Soluble Solids Content value,with features extracted from the preprocessed near-infrared spectrum of apple serving as model inputs.Considering the uncertainty in grading labels,mass generation approach and evidential encoding scheme for ordinal label are proposed,with uncertainty handled within the framework of Dempster–Shafer theory.Constructing neural network with ordered partitions as the base learner,the learning procedure of the Bagging-based ensemble model is detailed.Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

Dominance-based rough set approach as a paradigm of knowledge discovery and granular computing

Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.

Effects of Topographical and Edaphic Factors on the Distribution of Plant Communities in two Subtropical Karst Forests,Southwestern China

Relationships between topography,soil properties and the distribution of plant communities on two different rocky hillsides are examined in two subtropical karst forests in the Maolan National Natural Reserve,southwestern China.Surveys of two 1-ha permanent plots at each forest,and measurements of four topographic and thirteen edaphic factors on the slopes were performed.Twoway Indicator Species Analysis(TWINSPAN) and Detrended Canonical Correspondence Analysis(DCCA) were used for the classification of plant communities and for vegetation ordination with environmental variables.One hundred 10m×10m quadrats in each plot were classified into four plant community types.A clear altitudinal gradient suggested that elevation was important in community differentiation.The topography and soil explained 51.06% and 54.69% of the variability of the distribution of plant species in the two forest plots,respectively,indicating both topographic factors(eg.elevation,slope and rock-bareness rate) and edaphic factors(e.g.total P,K and exchangeable Ca) were the important drivers of the distribution of woody plant species in subtropical karst forest.However,our results suggested that topographical factors were more important than edaphic ones in affecting local plant distribution on steep slopes with extensive rock outcrops,while edaphic factors were more influential on gentle slope and relatively thick soil over rock in subtropical karst forest.Understanding relationships between vegetation and environmental factors in karst forest ecosystems would enable us to apply these findings in vegetation management strategies and restoration of forest communities.