Cost-sensitive decision tree ensembles for effective imbalanced classification

Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets

Ensemble learning for data stream analysis: a survey

In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research

Synthesis of peroxytetradecanoic acid.

<p>The peroxytetradecanoic acid is formed in the reaction of tetradecanoic acid with hydrogen peroxide.</p

Oxidation steps of PTP catalytic cysteine residue.

<p>The cysteine residue exists in a thiolate anion form and may undergo oxidation to the inactive sulfenic acid residue form. This conversion is reversible, but highly oxidizing conditions can further induce oxidation to the sulfinic and sulfonic acid residues, which is considered irreversible under physiological conditions.</p

The calculated binding affinities to CD45 catalytic center.

<p>The binding affinity of the peroxytetradecanoic acid, hydrogen peroxide and phosphotyrosine (natural substrate as a control) calculated with docking software AutoDock Vina version 1.1.1. The receptor structure used for affinity calculations was based on the PDB structure 1YGU and ligands were drawn in ChemDraw and processed with Schrodinger LigPrep version 25111. The data present the means of 6 repetitions with different random seeds.</p

The best predicted binding poses of ligands in the CD45 active site.

<p>The best predicted binding poses for peroxy acid C14 (panel A) and hydrogen peroxide (panel B) in the CD45 active site. The receptor was based on the CD45 D1 domain from PDB structure 1YGU <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052495#pone.0052495-Nam1" target="_blank">[20]</a>, with residues mutated to correspond to a CD45 reference sequence (UniProtKB accession number P08575) using the SWISS-MODEL web server <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052495#pone.0052495-Arnold1" target="_blank">[11]</a>. The docking was performed with the AutoDock Vina version 1.1.1 software <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052495#pone.0052495-Trott1" target="_blank">[15]</a> using a rigid receptor and a binding box centered on the CD45 phosphatase active site. The best binding pose was defined as the pose with the strongest affinity in the largest cluster of poses, with poses clustered with a 1.5 Å RMSD thresholds. Also highlighted are four important residues involved in binding (Tyr683, His822, Arg859 and Gln897) and the catalytic cysteine (Cys853). Predicted hydrogen bonds with a 3.5 Å distance cutoff are shown as green dashed lines. The residues are numbered according the P08575 sequence.</p

The enzymatic activity of PTP CD45.

<p>The effect of tetradecanoic acid, peroxytetradecanoic acid and H₂O₂ on enzymatic activity of PTP CD45 before and after treatment with DTT. The enzyme CD45 (130 nM) was incubated with 50 nM tetradecanoic acid, peroxytetradecanoic acid and H₂O₂ at 37°C for 15 minutes. The activity was measured after adding 1 mM pNPP in 50 mM Tris buffer, pH 7.4. After the following 5 min of incubation, PTP activity was measured using microplate reader at 405 nm. Subsequently 20 mM dithiothreitol (DTT) was added to each sample for 30 minutes and a potential recovery of the enzymatic activity was assessed using the same technique. The results were expressed as percent of activity of control sample in Tris buffer. The data from three independent experiments were present as mean ± S.E.M; * significantly different (P<0.05) from control, ** significantly different (P<0.001) from control. The data were analysed using the combination of ANOVA and Tukey’s test (GraphPad Prism Software v.4).</p

GA affected HSPs gene expression in OS 143B cells.

<p>OS 143B cells were treated with GA (4 µM) for 6 h; the levels of Hsp60, Hsp70, Hsp90AA1, Hsp90AB1, Hsp90B1 transcript were then determined by means of Real Time PCR. Treatment with GA resulted in upregulation of Hsp60 (A), Hsp70 (B), Hsp90AA1 (C), Hsp90AB1 (D) transcript, however it did not impact Hsp90B1 gene expression (E). Values are mean ± SE of three independent experiments, relative mRNA levels of HSP/beta-actin are presented. The data were analyzed by Student's t-test using GraphPad Prism Software version 6.02. *P<0.01, **P<0.001 vs. control.</p

The effect of GA on proliferation and induction of cell death of OS 143B cells.

<p><b>A</b>. GA inhibits OS 143B cell growth. OS 143B cells were treated for 24 h with serial GA dilutions (within the range of 0.8 µM–50 µM). The cell viability was then determined by means of MTT assay. Data from at least three independent experiments are presented as mean ± SE. The absence of error bar denotes a line thickness greater than the error. Data were analyzed by GraphPad Prism Software version 6.02 performing One-way ANOVA combined with Dunett's Multiple Comparison Test. *P<0.01 vs. control. <b>B–D</b>. GA induced cell death of OS 143B cells. 143B OS cells were treated with 4 µM GA for 24 h, the cells were then harvested and the percentage of apoptotic and necrotic cells was determined performing double PI-Annexin V staining. <b>B</b>. Annexin V, PI-live/dead dot plots showing apoptosis and necrosis before and after treatment with GA. Plots are representative of five individual experiments. <b>C–D</b>. Total apoptotic (C) and necrotic (D) cell number before and after treatment with GA. Data from at least three independent experiments are presented as mean ± SE. Data were analyzed using GraphPad Prism (GraphPad Software, Inc., version 6.02, USA). Significant differences between groups were determined by Student's t-test. *P<0.01, ***P<0.0001 vs. control.</p

GA affected HSPs protein levels in OS 143B cells.

<p>OS 143B cells were treated with 4 µM GA for 6 h; the levels of Hsp60, Hsp70, total cellular Hsp90, Hsp90AA1, Hsp90AB1proteins were determined by Western blotting. <b>A</b>. GA decreased the level of Hsp60 and induced post-translational modification of Hsp60 partially derived from the hyperacetylated isoform. <b>B–E</b>. GA upregulated Hsp70 (B), total cellular Hsp90 (C), Hsp90AA1 (D), Hsp90AB1 (E) protein levels. Each experiment was performed at least three times. The representative data are shown. Densitometric analysis of HSP/beta-actin was performed using Quantity one 4.5.2 software.</p